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‫الجامعة اإلســالمية بغزة‬
‫عمادة الدراســـات العليا‬
‫ُكليـة الهندســـــــــــــــة‬
‫قسم الهندسة الكهربائية‬
The Islamic University of Gaza
Deanship of Post Graduated Studies
Faculty of Engineering
Electrical Engineering Department
Modeling and High Precision Motion Control
of 3 DOF Parallel Delta Robot Manipulator
Eng. Hamdallah A. H. Alashqar
Prof. Dr. Mohammed T. Hussein
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master in
Electrical Engineering
Thesis approval
‫َّب ِز ْدنِي ِع ْل ًما‪‬‬
‫‪َ ‬وقُل ر ِّ‬
‫[طه‪]111 :‬‬
To the teacher of the world, leader of the nation and mercy of Allah to mankind,
Prophet Muhammad peace be upon him
To my lovely parents who are honor by this moment
To my Wife and my sweet daughter Lian
First and forever, all praise and thanks for Allah, who gave me the strength,
and patience to carry out this work in this good manner. I would like to
deeply thank my supervisor Prof. Dr. Mohammed Hussein for his assistance,
guidance, support, patience, and encouragement. I would like to deeply thank
my discussion committee members Dr. Hatem Elaydi and Dr. Iyad Abuhadrous for their assistance and encouragement. I would like to thank the
Islamic University of Gaza providing me a good academic environment. In
addition, I would like to acknowledge the Academic staff of Electrical
Engineering, who supports me to carry out this work. Finally, great thanks
go to my beloved family for their endless praying and continuous support.
This Master thesis describes the CAD- Modeling of the Parallel DELTA robot,
designed by Autodesk Inventor® software program. DELTA Robot is a MultiInput Multi Output Nonlinear System (MIMO), so, PID controller and Model
Predictive Controller (MPC) are implemented to improve the performance of
Robot .but due to the variations in the dynamic models of each system, it is
nearly impossible to conclusively determine the most appropriate controller
to design. Therefore, this thesis compares the simulation results of two
controllers, namely the PID and MPC respectively; on a 3 DOF Parallel
DELTA robot in order to determine which controller would yield the best
control performance.
By comparing the simulation results for the joint angles error and the end
effector trajectory error plots for the PID and MPC controllers, MPC
controller gave the best results than PID controller. Then, a great
contribution added at the response of DELTA robot. Because of Robot arms
are highly geared; this reason let the robot to be more robust. MPC controller
held the Potential to be the most likely candidate controllers to implement on
the physical structure of the 3-DOF Parallel DELTA robot. But PID
controller is easier in software implementation inside embedded systems as
‫التمثيل الرياضي و التحكم عالي الدقة بالروبوت المتوازي "دلتا" ثالثي األبعاد‬
‫اقترحت في هذه الرسالة التمثيل الرياضي للروبوت عن طريق التصميم ثالثي األبعاد باستخدام برنامج‬
‫( ‪.)Autodesk Inventor‬الروبوت عبارة عن نظام متعدد المداخل ومتعدد المخارج غير خطي ‪ ,‬لذلك‬
‫فهو بحاجة الي نظام تحكم (‪ ) PID‬أو ( ‪ ) MBC‬لتحسين مستوي الدقة واألداء للروبوت‪ .‬كل نظام له‬
‫نموذج ديناميكي خاص به ‪ ,‬ل ذلك يصعب تحديد نوع المتحكم لكل نظام بدقة ‪ .‬لذلك هذه الرسالة تقارن نتائج‬
‫تطبيق نوعين من المتحكمات (‪ )PID‬و (‪ )MBC‬علي الروبوت المتوازي دلتا ثالثي األبعاد واختيار‬
‫المتحكم الذي يضمن للروبوت األداء األفضل‪.‬‬
‫بعد تمثيل ومقارنة النتائج المتوقعة من كال نظامي التحكم (‪ )PID‬ونظام التحكم (‪ , )MBC‬اثبتت النتائج‬
‫اإلفتراضية بأن كال المتحكمين أضافا تحسينا ً كبيرا ً علي حركة الروبوت لكن المتحكم (‪ )MBC‬أعطي‬
‫نتائج أفضل من المتحكم (‪ . )PID‬لكن بالرغم من هذا فإن نظام التحكم (‪ )PID‬أفضل في التمثيل والتنفيذ‬
‫عمليا علي الروبوت دلتا وذلك لسهولة برمجته علي انضمة التحكم الدقيق ( المايكروكنترولر)‪.‬‬
Table of Contents:
List of Figures
List of Tables
List of Abbreviations
Introduction …………………………………………………..
Objectives and Methodology ……………………………………….
Problem Statement…………………………………………………..
Thesis Contribution………………………………………………….
Literature Review…………………………………………………….
Thesis Overview……………………………………………………...
Kinematics and Dynamics………………………………… 8
DELTA Type Parallel Robot………………………………………..
Inverse Kinematics…………………………………………………..
Forward Kinematics………………………………………………….
Velocity Kinematics………………………………………………….
Forward and Inverse Singularity analysis
Dynamic Equations…………………………………………………..
2.6.1 Virtual Work Dynamics……………………………………..
2.6.2 No-Rigid Body Effects……………………………………….
2.7 Actuator Dynamics…………………………………………………...
Controller Design
Controller Techniques …………………………………………….
Open and Closed-Loop Control…………………………………..
3.2.1 Robot Control Algorithms………………………………………...
Computed Torque Control………………………………………...
PID Outer-Loop Designs…………………………………………..
PD-Plus-Gravity Controller……………………………………….
Optimal PD Controller Design……………………………………
Model Predictive Control (MPC)………………………...............
Path Planning
Cubic Polynomials…………………………………………….
4.2.1 Cubic Polynomials for a path with via points………….
Simulations and Results
Introduction………………………………………………………… 53
Modeling Multi-body Systems…………………………………...
DELTA Robot CAD Modelling…………………………………..
Dynamic Model of Delta Robot…………………………………..
Model Linearization……………………………………………….
5.5.1 Trimming and Linearizing Through Inverse Dynamics…...
5.5.2 Linearizing at an Operating Point…………………………….
Closed Loop Step Response………………………………………
Classical PID Controller………………………………………….
5.7.1 Joint Angles……………………………………………………..
5.7.2 Controller Output – Torque…………………………………..
Actuator Simulation……………………………………………….
Actuated DELTA Robot Simulation…………………………….
5.9.1 Joint Angles……………………………………………………..
5.10 Trajectory Generation…………………………………………….
5.11 End Effector Trajectory…………………………………………...
5.12 Model Predictive Controller (MPC) Simulation………………
5.12.1 Joint Angles……………………………………………………..
5.13 Recommended Controller…………………………………………
5.14 Simulation Discussion …………………………………………….
Conclusion and Future Work
Future Work…………………………………………………………. 82
List of Figures
DELTA Robot Control System……………………………………………
(a) Hexapod Parallel Robot, (b) CODIAN Robotics Parallel robot…
SCARA-type serial-architecture robot………………………………….
Delta Parallel Robot Flex Picker of ABB……………………………….
My Thesis Delta Parallel Robot side View Designed on Autodesk
One link side view of DELTA Parallel Robot…………………………..
First kinematics chain, XZ plane projection…………………………..
Two possible configurations of the kinematics chain due to θ1……..
Configuration chosen for direct kinematics analysis…………………
Two spheres intersect in a circle and a third sphere intersect the
circle at two places……………………………………………………….....
Projection of link i on xi zi plane, (b) end on view………………………
DC motor model…………………………………………………………..
Illustration of feedback control algorithm………………………………
Block diagram of computed torque control……………………………...
Basic Structure of MPC Controller……………………………………….
Model Used For Optimization………………………………………….....
Several possible path shapes for a single joint………………………..
Via points with desired velocities indicated by tangents………….....
CAD model of DELTA Robot………………………………………………
Dynamic Model of Delta Robot…………………………………………..
Single Arm Dynamic Model……………………………………………….
Inverse Dynamics DELTA Robot Model…………………………………
Linearization Simulink Model…………………………………………….
Simulink Model with No Controller……………………………………
phase’s response at Joints q1, q2 and q3……………………………......
Delta Robot Model with PID controller………………………………….
Joint Angle error for PID controller…………………………………......
q1 Joint Angle for PD controller in Table 5.3…………………………
Torque output for PID controller for q1 Joint…………………………..
DC motor with simple gear Model…………………………………….....
Rotor’s shaft speed V.S gear’s shaft speed for DC Motor……………
Rotor’s shaft torque V.S gear’s shaft torque for DC motor……………
Linearized Delta Robot model with DC motor Actuators…………
Voltage pulse train Response for joint qi……………………………
Circle path trajectory model……………………………………………..
Output circle for model in Fig.5.16…………………………………
Circle Path Trajectory for DELTA Robot……………………………
End Effector Trajectory for PID Controller………………………
End Effector Trajectory error on x-axis for PID Controller………
End Effector Trajectory error on y-axis for PID Controller……
End Effector Trajectory error on z-axis for PID Controller………
MPC Controller based – DELTA Robot Simulink Model………
Joint q1 Trajectory error for MPC Controller………………………
Joint q2 Trajectory error for MPC Controller………………………
Joint q3 Trajectory error for MPC Controller………………………
Linearized Delta Robot Model with PID and MPC Controllers…….
q1 Joint Responses with PID and MPC Controllers………………….
List of Tables
Delta Robot Mechanical Parts properties……………………………
Delta Robot 3-D dimensions……………………………………………
PID Controller Parameters……………………………………………..
DC motor electrical and mechanical Parameterization……………
PID controller parameters……………………………………………..
PID controller parameters……………………………………………..
Comparison table between PID and MPC controller responses…..
List of Abbreviations
Tool Center Point
Degree of Freedom
Reference frame at the base plate.
Proportional Integral Derivative Control
Model Predictive Control
Jacobian matrix in Cartesian space
Jacobian matrix in joint space
Personal Computer
Chapter 1
There are essentially two types of robot manipulators: serial and parallel.
Serial manipulators consist of a number of links connected in series to one
another to form a kinematic chain. Each joint of the kinematic chain is
usually actuated. This type of structure is known as an open chained
mechanism. Parallel manipulators, on the other hand, consist of a number of
kinematic chains connected in parallel to one another. The kinematic chains
work in unison to move a common point. This common point usually consists
of a manipulator that performs a certain task. For the purpose of the three
degrees of freedom (3 DOF) parallel DELTA robot system described in this
thesis, the common point will also be referred to as the end effector. Since the
kinematic chains are eventually connected to a common point, a parallel
manipulator is considered a closed chained mechanism. The actuators in
parallel manipulators are usually located at the base or close to the base of
the system, which is in stark contrast to serial manipulators which have
actuators at every joint. The advantages of this type of configuration include
the fact that it could achieve a higher load capacity due to the decrease in the
mass of the overall system, it can produce high accelerations at the end
effector and it has a high mechanical stiffness to weight ratio [1].
The disadvantages of this type of configuration include the fact that the
dynamic model is quite complex in nature and there are many instances of
singularities that must be mapped out and avoided in order to maintain
control of the system. Parallel robots come in a wide variety of designs and
applications ranging from the Stewart platform or Hexapod Parallel Robot
shown in Fig. 1.1.a, which is used in aircraft motion simulators to the Delta
robot, which is used in packaging plants. This endows the fact that there
cannot be a conclusive result as to which controller best suits the
functionality of all parallel robots. Therefore, it is logical to experiment with
various control techniques to observe upon which controller would garner the
most satisfactory results based on a specific mechanical system.
This thesis presents the reader with the simulation results obtained from the
implementation of PID control and Model Predictive Control (MPC) on 3 DOF
Parallel DELTA robots. The parameters of the dynamic model of this system
are derived in detail followed by the derivation of the inverse kinematics of
the mechanical model. The non-singular region is then defined based on the
results obtained in the inverse kinematics. It is important to map out the
non-singular region since it is the only location in which the parallel robot is
able to operate under stable conditions. If the parallel robot were to enter a
singular region, it would render the controller ineffective and cause the entire
system to become unstable. It is impossible to adequately design any
controllers for the parallel robot without a clear understanding of the
dynamic model and the inverse kinematics of the mechanical model.
In recent years the number of studies and applications of parallel robots have
increased. One of the most popular applications is in industry packaging. The
above is due to their ease of construction, the lightness of their structure and
the high accelerations obtained by these devices.
Unlike the serial-type robot manipulators, which only have an open-loop
kinematic chain, parallel configuration allows for a distribution of payload
among their two, or more closed-loop, kinematic chains. To illustrate this
point consider Fig.1.1.a shows a parallel-architecture robot, used for object
loading and unloading. Fig.1.2 shows a SCARA-type serial-architecture robot.
By comparing the images it is easy to appreciate the difference between the
two types of architecture. In the case of the serial manipulator greater
robustness is required, as each link carries not only the weight of the
successive links but also the motors and payload. This creates a cantilever
effect in each link and, as a result, a greater deformation overall. In contrast,
in the parallel architecture the actuators are fixed to the base of the
manipulator so that the weight of the motors is not supported by the
kinematic chains. In addition, the payload is distributed among the
kinematic chains that con-form the manipulator. This results in thinner and
lighter kinematic chains, which in turn results in an increased payload
capacity of the manipulator, relative to its total mass.
Figure 1.1: (a) Hexapod Parallel Robot, (b) CODIAN Robotics Parallel robot
Figure1.2: SCARA-type serial-architecture robot
A disadvantage of parallel robots is their typically low cost effectiveness,
based on complex kinematics and rather expensive control units, as well as
the poor workspace to robot-dimension ratio [1].
On the other hand, the advantages of parallel robots stated before indicate
that their capabilities can be optimally oriented if their specifications are
task-adapted to the desired application. To facilitate flexibility and to enlarge
the field of application, it is reasonable to use a reconfigurable robot design.
This will also help to overcome the typical challenges of parallel robots, such
as high costs and undersized workspaces.
1.2 Motivation
Modeling and Digital control of Dynamic system was my target of my thesis;
Autodesk Inventor Software has a power capability in modeling of
mechanical systems. No need to extract the dynamic equations of robot.
Simulink tool of Matlab can simulate the body of the robot as built in
Autodesk Inventor software program.
This Master Thesis treats the modeling of the Parallel DELTA robot
actuated with Servo DC motors and drive units. Also the kinematics for a
Delta-3 robot is implemented to be able to see where the traveling plate has
for position for different arm angle configurations.
1.3 Objectives and Methodology
Objectives of this thesis can be summarized as follows:
Design and Building three legs Delta Parallel Robot: therefor, Delta Robot
was designed via Autodesk Inventor software program.
Forward and inverse kinematics analysis: Both forward and inverse
kinematic algorithms have been developed, which are essential for the motion
planning and control of a parallel robot.
Workspace analysis: It is necessary to ensure that delta parallel robot has a
reasonable workspace volume. Workspace analysis is also required in the
design of the parallel robot. Hence, a workspace visualization scheme has
been developed for the modular parallel robot system. PID and MPC
controllers are used to improve the end effector path tracking for the DELTA
1.4 Problem Statement
The optimal control problem can be stated as: find a closed loop optimal
controller that minimizes error between the measured phase and actual
phase wanted to track specified path. Optical encoder is attached in the end
of each Servo motor shaft, measuring the actual phase of the link and from
that; we can calculate radius speed and acceleration.
Controlling of Delta Parallel robot wants true modeling for its dynamics, so,
by using Autodesk Inventor program, we can model the robot easily and test
its motion in Simulink Matlab tool.
Figure 1.3: DELTA Robot Control System.
The three Drivers control one motor each to actuate the three arms at the
Delta-3 robot. From Fig.1.3, Controller unit calculates inverse kinematics of
reference position x,y,z of moving platform in delta robot , actuators drive
servo motors under the effect of PID controller leading the end effector to the
target point in minimum time and no error as possible .
1.5 Thesis Contribution
In this thesis, a new mechanical model of Delta parallel Robot was
introduced, and a digital Controller system based on microcontroller chips
are interfaced directly to PC computer via serial communication. Simulink
Matlab tool can communicate by hardware Controller unit, which compute
the kinematics of delta robot in place of Microcontroller. This model opens a
new road to master students to use other control systems and contribute the
motion precision of the moving platform.
1.6 Literature Review
Modeling and control of a Closed Chain Parallel DELTA Robot is very
difficult especially, when using traditional methods in modeling.
 YangminLi, Qingsong Xu [3] proposed the simplified dynamic
equations derived via the virtual work principle on 3-TRC translational
parallel kinematic machine.
 André Olsson [4] describes the virtual work principle mathematical
modeling of a Delta-3 robot actuated by motors and drive units.
Experiments with comparison between the Simulink model and the
real robot are done.
 Angelo Liadis[5] proposed Lagrangian principle for modeling 2 DOF
parallel robot, and introduced eight controllers , fuzzy and non-fuzzy
controllers. Experiments with comparison between the Simulink model
and the real robot are done.
 Mohsen, Mahdi, Mersad [9] describes the Dynamics modeling and
trajectory tracking control of a new structure of spatial parallel robots
from Delta robots family. This paper compared implementation of
computed torque (C-T) method using adaptive Neuro-fuzzy controller
and conventional PD controller.
 Yangmin Li and Qingsong Xu [12] performed inverse dynamic modeling
based upon the principle of virtual work for medical Delta Robot. The
dynamic control uses computed torque method.
1.7 Thesis Overview
The purpose of this thesis is to determine the most appropriate controller to
implement on a 3 DOF DELTA parallel robot apparatus. Chapter 2 will
discuss and derive the equations for the modelling of the parallel robot using
the dynamic equations of the constrained system and the inverse kinematics
of the mechanical structure. Chapter 3 will consist of the derivations of PID
and Model Predictive Controllers (MPC). Chapter 4 describes the path
planning which the robot must follow to travel from point to another point in
Cartesian space. Chapter 5 will compare and analyze the simulation results
of each controller utilizing MATLAB. The plots of the joint angles and end
effector trajectory along with their respective errors and torque will be
compared between all the controllers and a generalized conclusion of these
simulation results will be garnered. Chapter 6 will entail the overall
recommendation of the candidate controller which best suits the needs of the
parallel robot system. A description of the improvements or additions that
can be executed in future research endeavors will be investigated to conclude
this thesis.
Chapter 2
Kinematics and Dynamics
As mentioned in the previous chapter, I introduced the advantages and
disadvantages of parallel robot and compared their performance with serial
type robots. In this chapter, I will study the kinematics of 3 DOF Parallel
DELTA robot.
2.1 DELTA Type Parallel Robot
The well-known Delta robot structure was proposed by R. Clavel in [2]. Fig.
2.1 shows the main components of this robot, which consists of three or four
closed-loop kinematic chains. The robot has three degrees of freedom.
The parallelograms ensure the constant orientation between the fixed and
the mobile platform, allowing only translation movements of the latter. The
end effector of the manipulator is located on the mobile platform [3].
Parallel Robot can move products in a three dimensional Cartesian
coordinate system.
Figure 2.1: Parallel DELTA Robot Components
The combination of the constrained motion of the three arms connecting the
traveling plate to the base plate ensues in a resulting 3 translator degrees of
freedom (DOF). As an option, with a rotating axis at the Tool Center Point
(TCP), four DOF are possible.
The Robot consists of, consider Fig.2.1:
Three Actuators.
Base plate.
Upper robot arm.
Lower robot arm (Forearm).
Rotation arm (optional, 4-DOF).
Travelling plate, TCP.
The upper robot arms are mounted direct to the actuators to guarantee high
stability. And the Three actuators are rigidly mounted on the base plate with
120° in between. Each of the three Lower robot arms consists of two parallel
bars, which connects the upper arm with the travelling Plate via ball joints.
Lower frictional forces result from this. The wear reduces respectively as a
result. To measure each motor shaft angle a Quadrature Optical encoder is
used. A fourth bar, rotational axes, is available for the robot mechanics as an
option. The actuator for this axis is then mounted on the upper side of the
robot base plate. The bar is connected directly to the tool and ensures for an
additional rotation motion [4].
2.2 Inverse Kinematics
The purpose of determining the inverse kinematics of this parallel robot is to
accurately model the angle produced at each joint at a specific location of the
e effector. This is advantageous for two main reasons; the first being that it is
relatively simple to define any reasonable trajectory for the end effector to
traverse and secondly, it can track different trajectories in a non-singular
region [5].
The constrained three degrees of freedom system shown in Fig. 2.3 will also
be applicable in this section. It should be noted that the parameters of the
overall system are known, which include: the range of the desired angles for
θ1, θ2 and θ3 respectively, the overall length of each upper link La and the
overall length of each lower link Lb. the desired location of the end effector in
the x and y axis respectively and the horizon distance between the two motor
shafts (c).
The problem of the Inverse kinematics solution is to find the actuators states
θ1, θ2, θ3, known the end-effector position (x, y, z). To find the inverse
kinematics solution let us refer to Fig. 2.3 Also, let consider the origin of the
reference system fixed on the platform and the axes such as depicted in Fig.
2.3 Note that the parallelogram has been considered as a single link (lb).
Figure 2.2: Delta Parallel Robot side View Designed on Autodesk Inventor.
Figure 2.3: one link side view of DELTA Parallel Robot.
Figure 2.4: First kinematics chain, XZ plane projection.
The analysis begins considering each kinematics chain separately, for the
first kinematics chain; shown in Fig.2.3, we make a projection to the X-Z
plane, which yields a vector closed loop as shown in Fig.2.4.
From Fig. 2.4, we have:
d12  d 22  lb'2
lb'2  lb2  y 2
In addition we have that:
OA  la cos(1 )   x  DC  d1
Solving for d1, yields:
d1  T  la cos(1 )
T  OA  x  DC
Also, from the geometry of Fig. 2.5 we have,
d2  z  la sin(1 )
Substituting (2.2), (2.3) and (2.4) in (2.1) and simplifying, we obtain:
2T1la cos(1 )  2 zla sin(1 )  K
K  la2  lb2  x02  z 2  T 2
Substituting in (2.5) the trigonometric identities,
cos(1 ) 
1 t 2
, sin(1 )=
, where, t=tan( 1 )
1 t
1 t
We obtain:
e1t 2  e2t  e3  0
e1  2Tla  K
e2  4 zla
e3  2Tla  K
Solving (2.6) for t yields,
e2  e22  4e1e3
1  2 tan
From the previous equations, we can conclude that,
1  f ( x, y, z )
Following the same procedure, the others two kinematics chains
configurations can be solved. We can take advantage of the symmetry of
Delta Robot and consider the fact that each kinematics chain is rotated 120
degree relative to each other. We could take the base the first kinematics
chain and multiply it by the rotation matrix (120O for θ2 and 240O for θ3) and then
apply the process used to solve the first kinematics chain. Once followed the
procedure described previously, the values of θ2 and θ3 can be found. In
general, there are a total of eight possible robot postures corresponding to a
given end-effector location [6].
 x  cos(a) -sin(a) 0  x 
 y= sin(a) cos(a) 0  y 
  
 
 z   0
 z 
x  cos( ).x  sin( ). y
ysin( ).x cos( ).y
z  z
Where  is the angle of rotation about z axis, From Eq. (2.10) yields:
2,3  f ( x, y, z)
Hence, there are generally two solutions of θ1 and therefore two configuration
of the kinematics chain Fig. 2.5 corresponding to each end-effector location.
When Eq. 2.7 yields a double root, the two links of the kinematics chain are
in a fully stretched-out or folded-back configuration named singular
configuration. When Eq. 2.7 yields no real solution, the specified end-effector
location is not reachable. Despite of the two possible solutions, only the
negative root have to be taken because the positive one could cause
interference between the elements of the robot as depicted in Fig 2.5.
Figure 2.5: Two possible configurations of the kinematics chain due to θ1.
The inverse kinematics solution is tested for special cases by examining Eq.
2.7: If, e22  4e1e3  0 then the circle swept by vector AB intersects the sphere
swept by vector BC in two locations. If, e22  4e1e3  0 , then the circle and sphere
are tangent, and the manipulator is in a singular position. If, e22  4e1e3  0 , then
the circle and the sphere do not intersects and there are no real solutions. If
e1=e2 =e3=0, then the circle lies on the sphere, and there are infinite number
of solutions [1].
2.3 Forward Kinematics
The forward kinematics also called the direct kinematics of a parallel
manipulator determines the (x, y, and z) position of the travel plate in baseframe, given the configuration of each angle θi of the actuated revolute joints,
see Fig.2.6.
Figure 2.6: Configuration chosen for direct kinematics analysis
Consider three spheres each with the center at the elbow Bi of each robot arm
chain, and with the forearms lengths lb as radius. The forward kinematic
model for a Parallel Delta Robot can then be calculated with help of the
intersection between these three spheres. When visualizing these three
spheres they will intersect at two places.
One intersection point where z is positive and one intersection point where z
coordinate is negative. Based on the base frame {R} where z -axis is positive
upwards the TCP will be the intersection point when z is negative. Fig. 2.7
shows the intersection between three spheres. Where two spheres intersects
in a circle and then the third sphere intersects this circle at two places.
Figure 2.7: two spheres intersect in a circle and a third sphere intersect the
circle at two places
Based on the model assumptions made, the vector Bi that describes the elbow
coordinates for each of the three arms as
Bi  [ f  la cos(i )
la sin(i )]T
To calculate the direct kinematics we move the center of the spheres to inside
from points
to the points
for i=1, 2 and 3 respectively. After this
transition the three spheres will intersect in the TCP point.
Bi'  [( f  e)  la cos(i )
la sin(i ]T
Where e = B1B1'  B2 B2'  B3 B3' is the length of shifted distance, clearly described
in Fig.2.6.
To achieve a matrix that describes all of the three points in the base frame
{O} one has to multiply Bi with the rotational matrix :
cos( )  sin( ) 0 
R 0z   sin( ) cos( ) 0 
 0
1 
The result is the matrix B' ,
cos( )  sin( ) 0 
B'  R 0z Bi'   sin( ) cos( ) 0  [( f  e)  la cos(i )
 0
1 
cos( ) Bi', x  cos( )[( f  e)  la cos(i )]  si , x 
 
B'   sin( ) Bi', x    sin( )[( f  e)  la cos(i )]    si ,y 
 Bi',z
 
  si ,z 
la sin(i )
la sin(i ]T
Then can three spheres be created with the forearms lengths lb as radius, and
their centers in Bi respectively. The general equation for a sphere is
( x  si , x )2  ( y  si ,y )2  ( z  si ,z )2  r 2
This gives the three equations for three links i =1,2 and 3 respectively. For
link (1) the upper arm is parallel to x- axis and perpendicular to y-axis, so the
rotation angle  =0, but the other two links have a rotation angles  = 120 for
link (2) and  = -120 for link (3).
( x  cos(1 )[( f  e)  la cos(1 )]) 2  ( y  sin(1 )[( f  e)  la cos(1 )]) 2  ( z  la sin(1 )) 2  lb 2
( x  cos( 2 )[( f  e)  la cos( 2 )]) 2  ( y  sin( 2 )[( f  e)  la cos( 2 )]) 2  ( z  la sin( 2 )) 2  lb 2
( x  cos( 3 )[( f  e)  la cos(3 )]) 2  ( y  sin( 3 )[( f  e)  la cos(3 )]) 2  ( z  la sin(3 )) 2  lb 2
After substitution the values 1 =0,  2 = 120, and  3 =-120 in Eq. 13, we get the
three sphere equations,
( x  [( f  e)  la cos(1 )]) 2  ( y ) 2  ( z  la sin(1 )) 2  lb 2
( x  [( f  e)  la cos( 2 )]) 2  ( y 
( x  [( f  e)  la cos(3 )]) 2  ( y 
[( f  e)  la cos( 2 )]) 2  ( z  la sin( 2 )) 2  lb 2
[( f  e)  la cos(3 )]) 2  ( z  la sin(3 )) 2  lb 2
Rearrange Eq. 2.18 we obtain,
( x  k11 ) 2  ( y  k12 ) 2  (z  k13 ) 2  lb 2
( x  k21 ) 2  ( y  k22 ) 2  ( z  k23 ) 2  lb 2
( x  k31 )  ( y  k32 )  ( z  k33 )  lb
k11  ( f  e)  la cos(1 )
k12  0
k13  la sin(1 )
k21  [( f  e)  la cos( 2 )]
k22  
[( f  e)  la cos( 2 )]
k23  la sin( 2 )
k31  [( f  e)  la cos(3 )]
k32 
[( f  e)  la cos(3 )]
k33  la sin(3 )
After expanding Eq. 2.19 we obtain,
x2  y 2  z 2  2ki1 x  2ki 2 y 2ki 3 z  lb 2  (ki21  ki22  ki23 ) , i=1,2,3
Subtract Eq.2.20 with i=2 from Eq. 2.20 with i=1, we obtain,
2(k11  k21 ) x  2(k12  k22 ) y 2(k13  k23 ) z  (k212  k222  k232 )  (k112  k122  k132 ) (2.21)
Subtract Eq.2.20 with i=3 from Eq. 2.20 with i=1, we obtain,
2(k11  k31 ) x  2(k12  k32 ) y 2(k13  k33 ) z  (k312  k322  k332 )  (k112  k122  k132 )
Simplifying Eq.2.21 and Eq.2.22 we obtain,
a1 x  b1 y  c1 z  d1
a2 x  b2 y  c2 z  d 2
a1  2(k11  k21 )
b1  2(k12  k22 )
c1  2(k13  k23 )
a2  2(k11  k31 )
b2  2(k12  k32 )
c2  2(k13  k33 )
d1  (k21
 k22
 k23
)  (k112  k122  k132 )
d 2  (k312  k322  k332 )  (k112  k122  k132 )
Arranging Eq.2.23 in a matrix form, we obtain,
 a1
 2
b1   x   d1  c1 z 
b2   y   d 2  c2 z 
Define   a1b2  a2b1 , then for case   0 ,
x  (d1  c1 z )b2  (d 2  c2 z )b1
 (b2 d1  b1d 2 )  (b1c2  b2c1 ) z
y  (d 2  c2 z )a2  (d1  c1 z )a1
 (a1d 2  a2 d1 )  (a2c1  a1c2 ) z
x b2 d1  b1d 2 b1c2  b2c1
y a1d 2  a2 d1 a2c1  a1c2
b2 d1  b1d 2
bc b c
, f2  1 2 2 1
bc b c
a c a c
fx  1 2 2 1 , f y  2 1 1 2
f1 
x  f1  f x z
y  f2  f y z
Substituting Eq. 2.26 in Eq.2.19 for i=3; we obtain,
(1  f x2  f y2 ) z2  2([ f x f1  f x k31 ]  [ f y f 2  f y k32 ]  k33 ) z  f112  f 222  k332  lb2  0
A  (1  f x2  f y2 )
B  2([ f x f1  f x k31 ]  [ f y f 2  f y k32 ]  k33 )
C  f112  f 222  k332  lb2
f11  f1  k31
f 22  f 2  k32
The solution of Equation Az 2  Bz  C  0 is well known as
 B  B 2  4 AC
From Eq.2.28, we can Evaluates Eq.2.26.
Mathematically neither forward nor inverse kinematics gives single solution.
Forward kinematics usually has two solutions, because the passive joint
angles formed between upper arm and lower arm are not determined by
kinematic equations. Then the solution that is within the robots work area
must be chosen. With the base frame {O} in this case, it will lead to the
solution with negative z coordinate.
The output solution has Four cases are possible:
1) Generic solution. The two solutions are realized at the intersection of a
circle and a sphere.
2) Singular solution. Once sphere is tangent to the circle of intersection of
the other two spheres, hence there is only one solution possible.
3) Singular solution. The center of any two spheres coincides, resulting in
an infinite number of solutions. This is an unlikely configuration for
most practical embodiments of the manipulator, except for the situation
when θ1 = θ2 = θ3 = π/2.
4) No solution. The three spheres do not intersect at a common point.
2.4 Velocity Kinematics
The most relevant loop should be picked up for the intended Jacobian
analysis. Let θ be the vector made up of actuated joint variables and P is the
position vector of the moving platform. Then
Figure 2.8: (a) Projection of link i on xi zi plane, (b) end on view
11 
 x
θ  1i  12  , P   y 
13 
 z 
The Jacobian matrix will be derived by differentiating the appropriate loop
closure equation and rearranging the result in the following form
11 
 Px  vx 
 
J θ 11   J P  Py  v y 
11 
 Pz  vz 
 
where vx, vy, and vz are the x, y, and z components of the velocity of the point
P on the moving platform in the xyz frame. In order to arrive at the above
form of the equation, we look at the loop OAiBiCiP. The corresponding
closure equation in the xiyizi frame is
OP + PCi = OAi + Ai Bi + BiCi
In the matrix form we can write it as
 Px cos i  Py cos i   f   e 
cos 1i 
sin 3i cos( 2i  1i ) 
 P sin   P cos     0   0  l  0   l 
sin 3i
i 
 x
    a
 b
  0  0
 sin 1i 
sin 3i cos( 2i  1i ) 
Time differentiation of this equation leads to the desired Jacobian equation.
The loop closure equation Eq.2.31 can be re-written as
(P + e) = f + ai + bi
Where ai and bi represents vectors Ai Bi and Bi C i respectively.
Differentiating Eq.2.33 with respect to time and using the fact that f is a
vector characterizing the fixed platform, and e is a vector characterizing the
moving platform
P = v = ai + b i
The linear velocities on the right hand side of Eq.2.34 can be readily
converted into the angular velocities by using the well-known identities.
v = wai ×ai + wbi ×bi
wai and wbi is the angular velocity of the link i. To eliminate wbi , it is
necessary to dot-multiply both sides of Eq. 2.35 and bi. Therefore
bi .v = wai . (ai ×bi )
Rewriting the vectors of Eq.2.36 in the xiyizi coordinate frame leads to
cos 1i 
sin 3i cos( 2i  1i ) 
ai  la  0  , bi  lb 
sin 3i
 sin 1i 
sin 3i cos( 2i  1i ) 
 0 
vx cos i  v y cos i 
wi   1i  , v   vx sin i  v y cos i 
 0 
Substituting the values of ai, bi,vi and v in Eq.2.36 leads to
jix vx  jiy vy  jiz vz  la sin 2i sin 3i1i
jix  cos(1i   2i )sin 3i cos i  cos 3i sin i
jiy  cos(1i   2i )sin 3i sin i  cos 3i cos i
jiz  sin(1i   2i )sin 3i
Expanding Eq.2.37 for i = 1, 2 and 3 yields three scalar equations which can
be assembled into a matrix form as
jx v = jqq
 j1x
jx   j2 x
 j3 x
j1z 
j2 z 
j3 z 
sin  21 sin 31
jq  la 
sin  22 sin 32
j1 y
j2 y
j3 y
q  11 12 31 
sin  23 sin 33 
After algebraic manipulations, it is possible to write
v = Jq
 x
 1
 x
J = j-1x jq  
 1
 x
 1
 2
 2
 2
z 
3 
z 
3 
z 
3 
2.5 Forward and Inverse Singularity analysis
From Eq.2.38 it can be observed that singularity occurs:
1. when det(Jq) = 0. This means that either  2i = 0 or , 3i =0 or for i=1,2,3.
2. when det(Jx) = 0. This means that 1i  2i =0 or or 3i =0 or for i=1,2,3.
3. when det(Jq)=0 and det(Jx) =0. This situation occurs when 3i =0 or for
i=1,2 and 3.
In summary, singularity of the parallel manipulator occurs:
1. When all three pairs of the follower rods are parallel. Therefore, the
moving platform has three degrees of freedom and moves along a
spherical surface and rotates about the axis perpendicular to the
moving platform
2. When two pairs of the follower rods are parallel. The moving platform
has one degree of freedom; i.e. the moving platform moves in one
direction only.
3. When two pairs of the follower rods are in the same plane or two
parallel planes. The moving platform has one degree of freedom; i.e. the
moving platform rotates about the horizontal axis only.
2.6 Dynamic Equations
Dynamics is the science of motion. It describes why and how a motion occurs
when forces and moments are applied on massive bodies. The motion can be
considered as evolution of the position, orientation, and their time
derivatives. In robotics, the dynamic equation of motion for manipulators is
utilized to set up the fundamental equations for control. The links and arms
in a robotic system are modeled as rigid bodies.
Therefore, the dynamic properties of the rigid body take a central place in
robot dynamics. Since the arms of a robot may rotate or translate with
respect to each other, translational and rotational equations of motion must
be developed and described in body-attached coordinate frames B1, B2, B3 …
or in the global reference frame G.
There are basically two problems in robot dynamics.
Problem1. We want the links of a robot to move in a specified manner. What
forces and moments are required to achieve the motion?
The first Problem is called direct dynamics and is easier to solve when the
equations of motion are in hand because it needs differentiating of kinematics
equations. The first problem includes robots statics because the specified
motion can be the rest of a robot. In this condition, the problem reduces
finding forces such that no motion takes place when they act. However, there
are many meaningful problems of the first type that involve robot motion
rather than rest. An important example is that of finding the required forces
that must act on a robot such that its end-effector moves on a given path and
with a prescribed time history from the start configuration to the final
Problem2. The applied forces and moments on a robot are completely
specified. How will the robot move?
The second problem is called inverse dynamics and is more difficult to solve
since it needs integration of equations of motion. However, the variety of the
applied problems of the second type is interesting. Problem 2 is essentially a
prediction since we wish to find the robot motion for all future times when
the initial state of each link is given.
In this section, we will perform the inverse dynamic modeling of the parallel
manipulator based upon the principle of virtual work. The inverse dynamics
problem is to find the actuator torques and/or forces required to generate a
desired trajectory of the manipulator.[theory of applied robotics boo]
It is often convenient to express the dynamic equations of a manipulator in a
single equation that hides some of the details, but shows some of the
structure of the equations. The state-space equation When the Newton—
Euler equations are evaluated symbolically for any manipulator, they yield a
dynamic equation that can be written in the form
τ  M( )  V( , )  G( )
where M( ) is n x n mass matrix of the manipulator, V( , ) is a n x 1
vector of centrifugal and Coriolis terms, and G( ) is an n x 1 vector of gravity
terms. We use the term state-space equation because the term V ( , ) has both
position and velocity dependence. Each element of M( ) and G ( ) is a
complex function that depends on θ, the position of all the joints of the
manipulator. Each element of V( , ) is a complex function of both  and  .
We may separate the various types of terms appearing in the dynamic
equations and form the mass matrix of the manipulator, the centrifugal and
Coriolis vector, and the gravity vector [1].
2.6.1 Virtual Work Dynamics
In this section, we will perform the inverse dynamic modeling of the parallel
manipulator based upon the principle of virtual work. The inverse dynamics
problem is to find the actuator torques and/or forces required to generate a
desired trajectory of the manipulator [9].
Without losing generality of model, we can simplify the dynamic problem by
the following hypotheses:
The connecting rods of lower links can be built with light materials such as
the aluminum alloy, so
 The lower links rotational inertias are neglected.
 the mass of each lower links, is divided evenly and concentrated at
 The two endpoints of the parallelogram.
Also it is supposed that:
• The friction forces in joints are neglected.
• No external forces suffered.
We consider that   [1 , 2 , 3 ] and   [1 , 2 , 3 ] are the vector of
actuator torques and vector of corresponding virtual angular displacements.
Furthermore,  p  [ x,  y,  z ] represents the virtual linear displacements
vector of the mobile platform. We can derive the following equations by
applying the virtual work principle.
 T   M Ga
  FGp
 p  M aT   FpT  p  0
M Ga  ( ma  mb ).g la .I.[cos(1 ) cos(2 ) cos(3 )]T
is the upper links gravity torques vector ma and mb are mass of upper link
and each connecting rod of lower link, respectively. Here g denotes the
gravity acceleration, and I represent the 3x3 identity matrix.
FGp  [0 0  (mtcp  3mb ) g )]T
Denotes the mobile platform gravity force vector, and mtcp is mass of the
mobile platform.
M a  I a  I a [1 2 3 ]T
I a  ( mala 2  mbla 2 ).I
Represents the upper links inertia torques vector and denotes the upper links
inertial matrix with respect to the fixed frame O{x, y, z}, and,
FP  M P P  (mtcp  3mb ).I.[x y z]T
Denote the mobile platform inertial forces vector. Eq.2.39 in section 2.4 can
be rewritten to,
P = Jθ
 P  J 
Substituting Eq. 2.48 into Eq. 2.42 results,
( T  M Ga
 FGp
J  M aT  FpT J )  0
Eq. 2.49 holds for any virtual displacements  , so we have
  M a  J T Fp  M Ga  J T FGp
Substitute Eqs.2.44 and 2.45 into Eq. 2.50, allows the generation of
  I a  J T M p P  M Ga  J T FGp
Differentiating Eq. 2.47 with respect to time, yields
P  J  J
Substituting Eq. 2.52 into Eq. 2.51, we can derive that
  M ( )  V ( , )  G( )
The previous equation described in Eq. 2.41 represents the dynamic model of
parallel manipulator in joint space. Here,   R is the controlled variables,
M ( )  I a  J T M p J
Denotes a symmetric positive definite inertial matrix, that M ( )  R
3 x3
V ( , )  J T M p J
Where V ( , )  R3 x3 is the centrifugal and Coriolis forces matrix, and
G( )   MGa  J T FGp
Represents the vector of gravity forces, and G( )  R3 .
2.6.2 Non-Rigid Body Effects
It is important to realize that the dynamic equations we have derived do not
encompass all the effects acting on a manipulator. They include only those
forces which arise from rigid body mechanics. The most important source of
forces that are not included is friction. All mechanisms are, of course, affected
by frictional forces. In present-day manipulators, in which significant gearing
is typical, the forces due to friction can actually be quite large - perhaps
equaling 25% of the torque required to move the manipulator in typical
situations. In order to make dynamic equations reflect the reality of the
physical device, it is important to model (at least approximately) these forces
of friction. A very simple model for friction is viscous friction, in which the
torque due to friction is proportional to the velocity of joint motion. Thus, we
 friction  v
where v is a viscous-friction constant. Another possible simple model for
friction, Coulomb friction, is sometimes used. Coulomb friction is constant
except for a sign dependence on the joint velocity and is given by
 friction  c sgn( )
where c is a Coulomb-friction constant. The value of c is often taken at one
value when   0 the static coefficient, but at a lower value, the dynamic
coefficient, when   0 , whether a joint of a particular manipulator exhibits
viscous or Coulomb friction is a complicated issue of lubrication and other
effects. A reasonable model is to include both, because both effects are likely:
 friction  v  c sgn( )
It turns out that, in many manipulator joints, friction also displays a
dependence on the joint position. A major cause of this effect might be gears
that are not perfectly round-their eccentricity would cause friction to change
according to joint position. So a fairly complex friction model would have the
 friction  f ( , )
These friction models are then added to the other dynamic terms derived
from the rigid-body model, yielding the more complete model
  M ( )  V ( , )  G( )  F ( , )
There are also other effects, which are neglected in this model. For example,
the assumption of rigid body links means that we have failed to include
bending effects (which give rise to resonances) in our equations of motion.
However, these effects are extremely difficult to model and are beyond the
scope of this thesis [1].
2.7 Actuator Dynamics
The leg system is basically composed of dc motor, precision revolute bearing
and coupling elements. Dc motor model is given below
Figure 2.9: DC motor model
The symbols represent the following variables here  m is the motor position
(radian),  m is the produced torque by the motor (Nm),  1 is the load torque, va
is the armature voltage (V), La is the armature inductance (H), Ra is the
armature resistance (Ω), Em is the reverse EMF (V), Ia is the armature current
(A), Kb is the reverse EMF constant, Km is the torque constant [10].
 Ra ia  Va  Em
Em  K b m
 m  K mia
d 2 m
 m   1  jm 2
On the assumption of a rigid transmission and with no backlash the
relationship between the input forces (velocities) and the output forces
(velocities) are purely proportional. This gives,
m  Krl
Where, constant Kr is a parameter which describes the gear reduction ratio.  l
is the load torque at the robot axis and  m is the torque produced by the
actuator at the shaft axis. In view of Eq. 2.54 one can write
m 
To simulate the motion of a manipulator, we must make use of a model of the
dynamics such as the one we have just developed. Given the dynamics
written in closed form as in (2.52), simulation requires solving the dynamic
equation for acceleration:
  M 1 ( )[  V ( , )  G( )  F ( , )]
We can then apply any of several known numerical integration techniques to
integrate the acceleration to compute future positions and velocities. Given
initial conditions on the motion of the manipulator, usually in the form
 (0)  
 (0)  0
Chapter 3
Controller Design
3.1 Controller Techniques
Using inverse kinematics, we can calculate the joint kinematics for a desired
geometric path of the end-effector of a robot. Substitution of the joint
kinematics in equations of motion provides the actuator commands. Applying
the commands will move the end-effector of the robot on the desired path
ideally. However, because of perturbations and non-modeled phenomena, the
robot will not follow the desired path. The techniques that minimize or
remove the difference are called the control techniques [11].
3.2 Open and Closed-Loop Control
A robot is a mechanism with an actuator at each joint i to apply a force or
torque to derive the link ( i ). The robot is instrumented with position,
velocity, and possibly acceleration sensors to measure the joint variables’
kinematics. The measured values are usually kinematics information of the
frame Bi , attached to the link i . Relative to the frame Bi 1 or B0 . To cause each
joint of the robot to follow a desired motion, we must provide the required
torque command. Assume that the desired path of joint variables, qd  q(t ) are
given as functions of time. Then, the required torques that causes the robot to
follow the desired motion is calculated by the equations of motion and is
equal to
Qc  D(qd )qd  H(qd , qd )  G(qd )
Where the subscripts d and c stands for desired and controlled, respectively. in
an ideal world, the variables can be measured exactly and the robot can
perfectly work based on the equations of motion (3.1). Then, the actuators’
control command Qc can cause the desired path qd to happen. This is an openloop control algorithm, that the control commands are calculated based on a
known desired path and the equations of motion. Then, the control
commands are fed to the system to generate the desired path. Therefore, in
an open-loop control algorithm, we expect the robot to follow the designed
path, however, there is no mechanism to compensate any possible error.
Now assume that we are watching the robot during its motion by measuring
the joints’ kinematics. At any instant there can be a difference between the
actual joint variables and the desired values. The difference is called error
and is measured by
e  q  qd
e  q  qd
Let’s define a control law and calculate a new control command vector by
Q = Qc  Kd e  K pe
where kP and kd are constant control gains. The control law compares the
actual joint variables (q, q) with the desired values (qd , qd ) , and generates a
command proportionally. Applying the new control command changes the
dynamic equations of the robot to produce the actual joint variables q .
Qc  Kd e  K pe  D(q )q  H(q , q )  G(q )
Figure 3.1 Illustration of feedback control algorithm
Fig. 3.1 illustrates the idea of this control method in a block diagram. This is
a closed-loop control algorithm, in which the control commands are calculated
based on the difference between actual and desired variables. Reading the
actual variables and comparing with the desired values is called feedback, and
because of that, the closed-loop control algorithm is also called a feedback
control algorithm.
The controller provides a signal proportional to the error and its time rate.
This signal is added to the predicted command Qc to compensate the error.
The principle of feedback control can be expressed as: Increase the control
command when the actual variable is smaller than the desired value and decrease
the control command when the actual variable is larger than the desired value.
3.2.1 Robot Control Algorithms
Robots are nonlinear dynamical systems, and there is no general method for
designing a nonlinear controller to be suitable for every robot in every
mission. However, there are a variety of alternative and complementary
methods, each best applicable to particular class of robots in a particular
mission. The most important control methods are as follows:
 Feedback Linearization or Computed Torque Control Technique.
In feedback linearization technique, we define a control law to obtain a linear
differential equation for error command, and then use the linear control
design techniques. The feedback linearization technique can be applied to
robots successfully; however, it does not guarantee robustness according to
parameter uncertainty or disturbances. This technique is a model-based
control method, because the control law is designed based on a nominal
model of the robot.
 Linear Control Technique
The simplest technique for controlling robots is to design a linear controller
based on the linearization of the equations of motion about an operating
point. The linearization technique locally determines the stability of the
robot. Proportional, integral, and derivative, or any combination of them, are
the most practical linear control techniques.
 Adaptive Control Technique
Adaptive control is a technique for controlling uncertain or time-varying
robots. Adaptive control technique is more effective for low DOF robots.
 Robust and Adaptive Control Technique.
In the robust control method, the controller is designed based on the nominal
model plus some uncertainty. Uncertainty can be in any parameter, such as
the load carrying by the end-effector. For example, we develop a control
technique to be effective for loads in a range of 1 - 10 kg.
 Gain-Scheduling Control Technique.
Gain-scheduling is a technique that tries to apply the linear control
techniques to the nonlinear dynamics of robots. In gain-scheduling, we select
a number of control points to cover the range of robot operation. Then at each
control point, we make a linear time-varying approximation to the robot
dynamics and design a linear controller. The parameters of the controller are
then interpolated or scheduled between control points.
3.3 Computed Torque Control
Dynamics of a robot can be expressed in the form
Q  D(q)q  H(q, q)  G(q)  d
Where q is the vector of joint variables, and Q is the torques applied at joints,
And is  d a disturbance .Assume a desired path in joint space is given by a
twice differentiable function q  qd (t )  C 2 . Hence, the desired time history of
joints’ position, velocity, and acceleration are known [12].
We can re-write Eq. 3.5 to:
Q  D(q)q  N(q, q)  d
If this equation includes motor actuator dynamics, then Q is an input voltage.
Define an output or tracking error as:
e  qd  q
e  qd  q
e  qd  q
And so,
Solving now for
in Eq.3.6 and substituting into Eq. 3.7 yields,
e  qd  D1 (N d  )
And the disturbance function
w  D1 d
we may define a state x(t) by
 e 
Write the tracking error dynamics as,
 e  0
 
 e  0
  
I  e  0 0
  u    w
0 e   I   I 
It is driven by the control input u(t) and the disturbance w(t). Note that this
derivation is a special case of the general feedback linearization procedure.
The feedback linearizing transformation may be inverted to yield
  D(qd  u)  N
We call this the computed-torque control law. Substituting Eq. 3.13 into
Eq.3.5 yields
D(q)q  N(q, q)  d  D(qd  u)  N
e  u  D1 d
3.4 PID Outer-Loop Designs
One way to select the auxiliary control signal u(t) is as the proportional-plus
derivative (PD) feedback,
u  kve  kbe  ki
Then the overall robot arm input becomes
  D(q)(q  kve  kbe  ki )  N(q, q)
The closed loop error dynamics
e  kve  kbe  ki  w
The next diagram, represent the PD – Computed Torque controller
Figure 3.2. Block diagram of computed torque control.
3.5 PD-Plus-Gravity Controller
A useful controller in the computed-torque family is the PD-plus-gravity
controller that results when D=I, N=G(q)-qd, with G(q) the gravity term of the
manipulator dynamics. Then, selecting PD feedback for u(t) yields,
 c  kve  kbe  G(q)
When the arm is at rest, the only nonzero terms in the dynamics Eq.3.5 are
the gravity G (q), the disturbance  d , and possibly the control torque .
The PD-gravity controller  c , includes G (q), so that we should expect good
Performance for set-point tracking, that is, when a constant qd is given so
that qd = 0.
3.6 Optimal PD Controller Design
The goal of implementing any type of controller is to observe the output
response it would generate based on the inputted conditions. In order to
achieve this, it is necessary to solve for the control input (u) of the system.
Each controller has a different method pertaining to how this equation is
obtained, but the initial steps to reach this point are all similar.
The end effector of the three degrees of freedom parallel robot will follow a
predefined trajectory; hence for tracking control it is appropriate to set the
error and change in error as:
x1  1d  1 ,
x2   2 d   2 ,
x3   3d   3 ,
x4  1d  1 ,
x5   2 d   2 ,
x6   3d   3 ,
1d ,  2d and 3d are the desired angles; 1 ,  2 and 3 are the actual
angles; 1d ,  2d and  3d are the desired angular velocities, 1 ,  2 and  3 are the
actual angular velocities; 1d ,  2d and  3d the desired angular accelerations.
The following system is in lower triangular form, which can be produced by
differentiating x1 , x2 , x3 , x4 , x5 and x6 .
x1  x4
x2  x5
x3  x6
1 
 x4  1d 
 x      D 1 ( )  u  C ( , )    G ( ) 
 2
 5   2d 
3 
 x6  3d 
 
One aspect that constantly appears when implementing the appropriate
controller is the feed forward term ud. This term represents the desired
control input required in the overall system operation. In theory, the actual
and desired control input should be identical, but due to system disturbances
and the force of gravity, this is known not to be the case. By adding ud into
the specified controller, improved control performance can be achieved. It is
defined as:
ud  D(d )d  C (d ,d )d  G(d )
That is, a Lyapunov function is necessary in order to achieve the desired
results. Let this function candidate be:
V  0.5  x1
x3 
 kp1
 0
0 
0 
kp3 
 x1 
 x   0.5 x
 2
 x3 
 x4 
x6  D( )  x5 
 x6 
It should be noted that KP1 , KP2 and KP3 represent the proportional gains of
motors one , two and three respectively. The derivative of V with respect to
time becomes:
0 
 kp1 0
V   x1 x2 x3   0 kp2 0 
 0
0 kp3 
 x4 
+  x4 x5 x6  D( )  x5 
 x6 
 x4 
 x   0.5 x
 5
 x6 
 x4 
x6  D ( )  x5 
 x6 
As previously stated, D( )  2C ( , ) is skew symmetric; hence:
0.5  x4
 x4 
x6  D( )  x5    x4
 x6 
 x4 
x6  C ( ,  )  x5 
 x6 
Therefore, by substituting equations (3.3) and (3.25) into equation (3.24), it is
possible to achieve:
V   x1
x3 
 kp1 0
 0 kp
 0
0 
0 
kp3 
 x4 
x   x
 5  4
 x6 
1d 
1d 
 u1  
 
 
x6   D( )  2 d   C ( ,  ))  2 d   G ( )  u2  
3d 
3d 
u3  
 
 
With all the appropriate data defined, it is now possible to determine the
equation for the controller. The control effort must satisfy the condition of
convergence and it must ensure that the output response is stable. The
following controller was chosen to accomplish these requirements:
 u1   kp1 0
u  u2    0 kp2
u3   0
0 
0 
kp3 
 x1   kd1 0
 x    0 kd
 2 
 x3   0
0 
0   x4
kd3 
1d 
1d 
 
 
x6    D( )  2 d   C ( ,  )  2 d   G ( ) 
3d 
3d 
 
 
It should be noted that Kd1 , Kd2 and Kd3 represent the derivative gains of motors one,
two and three, respectively.
3.7 Model Predictive Control (MPC)
Model Predictive Control (MPC) is an optimal control strategy based on
numerical optimization. Future control inputs and future plant responses are
predicted using a system model and optimized at regular intervals with
respect to a performance index. From its origins as a computational
technique for improving control performance in applications within the
process and petrochemical industries, predictive control has become arguably
the most widespread advanced control methodology currently in use in
industry. MPC now has a sound theoretical basis and its stability, optimality,
and robustness properties are well understood.
The basic structure of MPC to implement is shown in Fig.3.3. A model is used
to predict the future plant outputs, based on past and current values and on
proposed future control actions.
Figure 3.3: Basic Structure of MPC Controller.
These actions are calculated by optimizer taking into account the cost
function as well as con- strains. The optimizer is another fundamental part of
the strategy as it provides the control action. The goal is to apply the linear
model predictive control to the input-output linearized system to account for
the constraints. Since the linear model predictive is more naturally
formulated in discrete time, the linear subsystem is discretized with a
sampling period T to yield,
 (k  1)  A d  (k)  Bd V (k)
y(k)  H d  (k)
The model consists of:
 A model of the plant to be controlled, whose inputs are the manipulated
variables, the measured disturbances, and the unmeasured
 A model generating the unmeasured disturbances
Figure 3.4: Model Used For Optimization
The model of the plant is a linear time-invariant system described by the
x(k  1)  Ax(k)  Bu u(k)  Bv v(k)  Bd d (k)
y(k)  C x x(k)  Dv v(k)  Dd d (k)
The unmeasured disturbance d(k) is modeled as the output of the linear time
invariant system:
x d (k  1)  Ax d (k)  Bn d (k)
d(k)  Cx d (k )  Dnd (k)
The system described by the above equations is driven by the random
Gaussian noise nd(k), having zero mean and unit covariance matrix.
Chapter 4
Path Planning
4.1 Introduction
In this chapter, we concern ourselves with methods of computing a trajectory
that describes the desired motion of a manipulator in multidimensional
space. Here, trajectory refers to a time history of position, velocity, and
acceleration for each degree of freedom.
This problem includes the human-interface problem of how we wish to specify
a trajectory or path through space. In order to make the description of
manipulator motion easy for a human user of a robot system, the user should
not be required to write down complicated functions of space and time to
specify the task. Rather, we must allow the capability of specifying
trajectories with simple descriptions of the desired motion, and let the system
figure out the details. For example, the user might want to be able to specify
nothing more than the desired goal position and orientation of the endeffector and leave it to the system to decide on the exact shape of the path to
get there, the duration, the velocity profile, and other details.
We also are concerned with how trajectories are represented in the computer
after they have been planned. Finally, there is the problem of actually
computing the trajectory from the internal representation—or generating the
Generation occurs at run time; in the most general case, position, velocity,
and acceleration are computed. These trajectories are computed on digital
computers, so the trajectory points are computed at a certain rate, called the
path-update rate. In typical manipulator systems, this rate lies between 60
and 2000 Hz [1].
4.2 Cubic polynomials
Consider the problem of moving the tool from its initial position to a goal
position in a certain amount of time. Inverse kinematics allows the set of
joint angles that correspond to the goal position and orientation to be
Figure 4.1: Several possible path shapes for a single joint.
The initial position of the manipulator is also known in the form of a set of
joint angles. What is required is a function for each joint whose value at t0 is
the initial position of the joint and whose value at tf is the desired goal
position of that joint. As shown in Fig. 4.1, there are many smooth functions,
 (t ) , that might be used to interpolate the joint value. Several possible path
shapes for a single joint. In making a single smooth motion, at least four
constraints on  (t ) are evident. Two constraints on the function's value come
from the selection of initial and final values:
 (0)  
 (t f )   f
An additional two constraints are that the function be continuous in velocity,
which in this case means that the initial and final velocities are zero:
 (0)  0
 (t f )  0
These four constraints can be satisfied by a polynomial of at least third
degree. These constraints uniquely specify a particular cubic. A cubic has the
 (t )  a  a1t  a2t 2  a3t 3
So, the joint velocity and acceleration along this path are clearly
 (t )  a1  2a2t  3a3t 2
 (t )  2a2  6a3t
Solving these equations for the ai we obtain
a0   0
a1  0
a2 
( f   0 )
t 2f
a3 
t 3f
Using Eq. 4.5, we can calculate the cubic polynomial that connects any initial
joint angle position with any desired final position. This solution is for the
case when the joint starts and finishes at zero velocity.
4.2.1 Cubic polynomials for a path with via points
So far, we have considered motions described by a desired duration and a
final goal point. In general, we wish to allow paths to be specified that
include intermediate via points. If the manipulator is to come to rest at each
via point, then we can use the cubic solution of Eq. 4.3.
Figure 4.2: Via points with desired velocities at the points indicated by
If desired velocities of the joints at the via points are known, then we can
construct cubic polynomials as before; now, however, the velocity constraints
at each end are not zero, but rather, some known velocity
 (0)  
 (t f )   f
Solving these equations for ai then we obtain
a0   0 ,
a1   0 ,
a2 
f ,
a3 
( f   0 )
t 3f
t 2f
Chapter 5
Simulations and Results
5.1 Introduction
Each controller discussed in this chapter will contain the simulation results
for: the error between the desired and actual actuated joint angles, the
location of the desired and actual end effector trajectory in Cartesian space
along with their respective positional output error and the overall system
torque required to achieve the actual results. A preliminary conclusion will
then be drawn based on the pros and cons of each control technique.
5.2 Modeling Multi-body Systems
SimMechanics tool enables you to create libraries of components that can be
reused in many different designs. You define bodies in terms of their mass,
inertia, and connection points. To create complex shapes, you assemble sets
of simple geometries, such as spheres, cylinders, and extrusions defined in
MATLAB, and SimMechanics calculates the resulting mass and inertia
automatically. The diagram that defines the body clearly indicates all
connections to the body, making it easy to see your system’s topology.
Parameters such as length and mass can be calculated using MATLAB
scripts and assigned using MATLAB variables.
You connect bodies using joints and constraints. These define the degrees
of freedom permitted between the bodies in your system, which dictate how
your system can move. You can define and connect actuators to these joints to
enable your system to move. Actuating these joints with electrical, hydraulic,
pneumatic, or other physical systems modeled using Simscape tool enables
you to model your entire multi-domain physical system within the Simulink
environment [13].
You can import a CAD assembly into SimMechanics using SimMechanics
Link. The mass and inertia of each part in the assembly are imported as rigid
bodies in SimMechanics. Geometry from the CAD assembly is saved to
geometry files and associated with the proper body in SimMechanics. The
mate definitions in the CAD assembly are imported as joints in the
SimMechanics model.
For SolidWorks, Pro/ENGINEER, and Autodesk Inventor models, you install
a plug-in that lets you save the CAD assembly as an XML file that can be
imported into SimMechanics. For other CAD systems, SimMechanics Link
provides an API that you can connect to the API of your CAD system.
The SimMechanics Import XML Schema enables you to import models into
SimMechanics from any CAD system or modeling environment that exports
an XML file that follows this schema.
5.3 DELTA Robot CAD Modelling
As mentioned in the last section, delta robot designed in Autodesk Inventor.
Table 5.1 summarizes the lengths, inertias and masses of the links and
moving platform.
Figure5.1: CAD model of Delta Robot
With the help of Autodesk Inventor program, the following tables were
utilized.Table 5.1, describes the Mechanical Properties of Robot parts. Masses
and inertia depends on the material type, so, for more flexibility and robot
speed, I chose little weight materials in building the Robot architecture.
Table 5.1: Delta Robot Mechanical Parts properties
Mechanical Part
Upper Arm
Moving Platform
Table5.2. summarizes the mechanical part lengths and offset translation
between frame {Oc} and frame {Or} as described in Fig. 5.2.
Table 5.2: Delta Robot 3-D dimensions
Offset length from Frame {Oc} to Joint Ji.
Length of the upper arm links.
Length of the Lower arm links.
Offset length from joint Ci to point D.
Offset length from Frame {Oc} to frame {Or}
in z- direction.
Offset length in z-direction from D point to
Center of Gravity of the moving platform.
5.4 Dynamic Model of Delta Robot
After exporting the *.XML file generated from Autodesk Inventor Program,
Matlab Simulink tool converted the exported *.XML file to Simulink model as
illustrated in Fig.5.2.
Figure5.2: Dynamic Model of Delta Robot
From the above Fig. 5.2 , the Simulink model represents the three arms of
the robot, each arm is forced with torque at each private joint, speed and
position measurements is calculated from each joint via joint sensors.
The next figure illustrates the Simulink block diagram of single arm which is
composed of Upper Arm, Forearms, and its actuated joint and passive joint.
From the single arm dynamic model figure, Fig. 5.3 , the upper tip of the
upper arm is actuated by Joint actuator which is placed in place of DC motor
model, where the recent one do the same job.
Figure5.3: Single Arm Dynamic Model
5.5 Model Linearization
Linear analysis tool embedded in Matlab Simulink, can deliver the linearized
model of the nonlinear MIMO Delta Robot system dynamics. For
linearization process, this block diagram shown in Fig.5.4 is connected. The
output linearized model is arranged as
X  Ax  Bu
Y  Cx  Du
Where A, B, C and D are the state space matrices.
And where, [ x1, x ]  [q , q ] , [ x , x ]  [q , q ] , and [ x , x ]  [q , q ] . Where
1 1
3 4
2 2
5 6
3 3
qi , qi are the joint phase and joint angular speed respectively
 x1 
 x1 
x 
x 
 2
 2
1 
 x3 
 x3 
X     [A6 X 6 ]   [B6 X 3]  2 
 x4 
 x4 
 3 
 x5 
 x5 
 
 
 6 
 x6 
 x1 
x 
 2
1 
 x3 
Y  [C3 X 6 ]    [D3 X 3 ]  2 
 x4 
 3 
 x5 
 
 x6 
5.5.1 Trimming and Linearizing Through Inverse
Trimming is determination of the forces/torques necessary to produce the
specified motion. These motion states constitute a trim or operating point.
Trimming problems can have one solution, more than one, or none.
Figure 5.4: Inverse Dynamics DELTA Robot Model.
Each DELTA Robot leg outputs the computed leg force needed to maintain
the motion specified by the motion actuation. After simulating the previous
model Fig. 5.4, the steady states Torques keeping the platform sill is,
, 2, 3 ]  [2.128
2.128 2.128]
5.5.2 Linearizing at an Operating Point
Analysis tab in Matlab Simulink, linear analysis tool, introduces a simplified
method for non-linear systems linearization. The state space result of
linearization at the operating point [2.128, 2.128, and 2.128] is stated in
Eq.5.2 to Eq.5.5.
Figure 5.5: Simulink Model for Linearization
From Fig. 5.5, Linearization was according to position vector feedback. The
output state spaces linear matrices A, B, C and D are,
 0.6618
A 
 0.1717
 3.0516
0 -4.6386
0 2.4943
0 4.9436
0 10.5497 0 
0 0.8218 0 
0 9.1532 0 
0 
 0
 0.8109 15.5792 0.8109 
 0
0 
 0
0 
 8.6789 4.5069 4.0151
 30.9897 0 40.9965 0 88.2854 0 
C   57.2958 0
 10.0065 0 100.6680 0 47.2894 0 
0 0 0 
D  0 0 0 
0 0 0 
5.6 Closed Loop Step Response
In this Simulink model, is tested and the response of the robot tracking
without adding of a controller to the system, the model and results are shown
below in the next two figures, Fig 5.6 and Fig.5.7
Figure 5.6: Simulink Model with No Controller
Figure 5.7: phase’s response at Joints q1, q2 and q3.
From the above figure , a concolusion was drawn that the MIMO Delta Robot
Dynamics can not reach the desired trajectory without help of suitable
controller. In the next sectin , the examines of a calssical PID controller
shown and illustrated in Fig.5.8.
5.7 Classical PID Controller
From Simulink library, a PID block is added to the Simulink model of my
project. This block linearize the plat seen by PID controller and check the
step response of the linearized plant. Fig.5.7 shows the Simulink model with
PID controller. You must pay attention that PID controller block cannot
linearize MIMO systems, therefore, one robot link was chosen for
linearization and Control. After that, the result of the controller parameters
was copied to the other two link controllers of Delta robot for actuation.
Figure 5.8: Delta Robot Model with PID controller.
5.7.1 Joint Angles
The following figures depict the error between the desired and actual joint
angles of q1, q2, and q3 respectively. These are the only three angles directly
controllable by the actuators of the parallel robot.
Figure 5.9: Joint Angle error for PID controller.
It is crucial that the difference between the desired angles and the actual
angles of q1, q2, and q3 be as small as possible, in order for the end effector
error to be minimized. Fig. 5.9 displays a clearly overshoot, approximately
15%. This value of overshoot is not desired in robotics in general. So, more
tuned parameters are got to reach the minimum overshoot. The next table 5.3
lists the values of PID parameters, KP, KI, KD, and N, which guarantee the
closed loop stability, and ensures the lowest value for overshoot and error.
Table 5.3: PID Controller Parameters
Controller Parameters
Tuned Values
The step response of the moving platform where, (q1,q2,q3) = (-20, -20,-20).
Figure 5.10: q1 Joint Angle for PD controller in Table 5.3.
The constraint that ensured unbiased results was the fact that the motors
utilized in the physical model of the parallel robot could output a maximum
torque of 5 Newton meters. This will be confirmed in the subsequent torque
5.7.2 Controller Output - Torque
The next figure illustrates the torque generated by the controller which will
move the links to their actual position. The next figure, Fig.5.11, displays the
output of PID controller during two seconds with PID parameters listed in
Table must note that no actuator is considered in the previous
Simulink model. Torque is already delivered by controller, in practical, this
situation is not true. The next Simulink models will consider the servo motor
Figure 5.11: Torque output for PID controller for q1 Joint.
5.8 Actuator Simulation
For Delta robot mechanics, each joint is actuated with a highly geared DC
servo Motors, their mechanical and electrical parameters are listed in table
5.4. I got the help of SimDrive tool embedded in Matlab. The following Figure
illustrates the Simulink model of DC motor with 31:1 gear ratio.
Figure 5.12: DC motor with simple gear Model.
The following table, table 5.4, summarizes the expected DC motor
Table 5.4: DC motor electrical and mechanical Parameterization
Electrical Parameterization
Armature Inductance (L)
No-load Speed (rpm)
Rated Speed at rated Load (rpm)
Rated Load (Mechanical Power/Watt)
Rated DC Supply Voltage(V)
Rotor Damping Parameterization
No-Load Current (I)
DC Supply Voltage When Measuring
No-Load Current (V)
Mechanical Parameterization
Rotor Inertia (kg*m^2)
Gear Ratio
Gear Inertia (kg*m^2)
For testing the behavior of the actuator dynamics, the next figures, Fig.5.13
and Fig. 5.14, illustrates the voltage step response for speed and torque, with
no load. Fig.5.13 illustrates the voltage step response of the geared DC motor,
with gear ratio 10:1. The dashed curve in Fig.5.13 shows the rotor speed. And
the other curve shows the gear’s shaft speed.
Figure 5.13: rotor’s shaft speed V.S gear’s shaft speed for DC Motor
Figure 5.14: rotor’s shaft torque V.S gear’s shaft torque for DC motor.
From Fig. 5.13 and Fig. 5.14, we notice that,
wf  r 1wB
 f  r B
Where WF, TF is the gear’s follower angular speed and torque respectively,
and WB, TB is the gear’s base angular speed and torque respectively. And r is
the gear ratio.
5.9 Actuated DELTA Robot Simulation
In the previous Simulink models, that torque generation is considered to be
the duty of the controller, but, in practical, the controller generates PWM
voltage varies from 0 volt to + 5 volt, which are the Microcontroller voltages.
H-bridge circuit amplifies the input PWM voltage and actuates the DC motor
directly. Each link is energized with highly geared servo DC motor. Fig.5.15,
illustrates the PWM voltage-based Simulink model.
Figure 5.15: Linearized Delta Robot model with DC motor Actuators.
You can see from Fig. 5.14 that the motor block gets the voltage as an input
and generates mechanical torque.
5.9.1 Joint Angles
Addition of a DC motor models in the simulink model , pushed me to re-tune
PID parameters to get the optimal one approximately. As shown in the next
Table 5.5: PID controller parameters
Controller Parameters
Tuned Values
Figure 5.16: voltage pulse train Response for joint qi.
The other two joints have the same response, because the step input is in the
z direction.
5.10 Trajectory Generation
The end effector of the three degrees of freedom parallel robot was simulated
to follow a circular trajectory based on the implementation of the desired
controller. The tracking speed utilized is defined by the angular velocity
formula: ω = 2πf, where f is the tracking frequency of the end effector. The
origin of the circle based on the Cartesian coordinate system in millimeters is
defined as (0, 0,z0), where the distance of travelling in z- direction .the radius
of the circular trajectory is 100 millimeters and the frequency implemented is
0.5 Hertz. It should be noted that the trajectory defined in this report never
impedes or approaches any singular point.
The next Simulink model generates a circular path trajectory in the x-y
Figure 5.17: Circle path trajectory model.
Figure 5.18: output circle for model in Fig.5.17
5.11 End Effector Trajectory
The next set of figures portrays the trajectory tracking of the end effector
along with its respective error between its desired and actual position. In
order to determine the actual location of the end effector, equations (2.7) and
(2.10) were employed to solve for q1, q2 and q3 respectively. The Inverse
kinematics equations were then applied based on all the available data to
definitively determine the actual location of the end effector in Cartesian
coordinates. The results from the joint angles of q1, q2 and q3 play a crucial
role in the determination of the actual location of the end effector, since a
small angular error would not cause a large deviation when compared to the
desired trajectory. The plots of the end effector error in the x-axis and y-axis
are shown in order to easily identify the severity of the absolute accuracy.
The next Simulink Model illustrates the End effector tracking for a circle lies
on X- Y plane with radius r= 100 mm.
Figure 5.19: Circle Path Trajectory for DELTA Robot.
The next figures display the End Effector error on x-axis, y-axis and z-axis
and display the End Effector trajectory.
Figure 5.20: End Effector Trajectory for PID Controller.
Figure 5.21: End Effector Trajectory error on x-axis for PID Controller.
Figure 5.22: End Effector Trajectory error on y-axis for PID Controller.
Figure 5.23: End Effector Trajectory error on z-axis for PID Controller.
Model Predictive Controller (MPC)
Model Predictive Control Toolbox™ provides tools for systematically
analyzing, designing, and tuning model predictive controllers. You can design
and simulate model predictive controllers using functions in MATLAB® or
blocks in Simulink®. You can set and modify the predictive model, control
and prediction horizons, input and output constraints, and weights. The
toolbox enables you to diagnose issues that could lead to run-time failures
and provides advice on changing weights and constraints to improve
performance and robustness. By running different scenarios in linear and
nonlinear simulations, you can evaluate controller performance. You can
adjust controller performance as it runs by tuning weights and varying
In the next Simulink Model, I will test the MPC controller on a linearized
model for DELTA robot and comparing the output results with the previous
PID controller outputs.
Figure 5.24: MPC Controller based – DELTA Robot Simulink Model
5.12.1 Joint Angles
The following figures depict the error between the desired and actual joint
angles of q1, q2, and q3 respectively using MPC Controller.
Figure 5.25: Joint q1 Trajectory error for MPC Controller.
Figure 5.26: Joint q2 Trajectory error for MPC Controller.
Figure 5.27: Joint q3 Trajectory error for MPC Controller.
We can notice that MPC controller gave the same results of PID controller
approximately. Then, a great contribution added at the response of DELTA
robot. Robot arms are highly geared; this reason let the robot to be more
5.13 Recommended Controller
The true decision for choosing the suitable controller between PID and MPC
controllers which satisfy the desired criteria is taken after running the next
Simulink model, as shown in Fig 5.28
Figure 5.28: Linearized Delta Robot Model with PID and MPC Controllers.
The values of PID parameters which are choose in the previous Simulink
model, listed in the following table 5.6.
Table 5.6: PID controller parameters
Controller Parameters
Tuned Values
Fig.5.29, illustrates the result of q1 joint response with input angle value
equal 10 degrees.
Figure 5.29: q1 Joint Responses with PID and MPC Controllers.
The following table, compare the output results for q1 joint responses with
PID and MPC Controllers.
Table 5.7: Comparison table between PID and MPC controller responses
Overshoot (%)
steady state error
Settling Time (sec.)
PID Controller
MPC Controller
We can conclude that the joint response has no overshoot value with MPC
controller comparing with PID controller as illustrated in Table 5.7.
5.14 Simulation Discussion
The final purpose of Simulink simulation is to test path tracking response of
DELTA robot. Building the Nonlinear Dynamic Model using Autodesk
Inventor program was the easiest solution to model the robot. Real and not
approximated parameters were introduced via CAD dynamic Modeling which
is one of the advantages of CAD modeling, but in the other side, the
simulation speed is low. The simulation speed of Mathematical dynamic
Model is more rapid than CAD dynamic model. Low simulation speed is
annoying problem.
SimMechanics tool introduced an easy way to actuate robot joints by joint
actuator block. Sensing computed torque, angle, angular speed, and
acceleration was by joint sensor. Sensing of Cartesian coordinates of the
moving platform was by Body sensor, which facilitated the process of
DELTA robot needed a high precision controller to control it, PID and MPC
controllers were added. At the output of these two controllers, the output
torque command was limited, because that Servo DC motors attached at each
actuated joint generate a limited torque. PID parameters in Table 5.6 were
optimally tuned by desired response block in Matlab Simulink. Fig. 5.28
shows that, with PID Controller and at an input angle of 10 degrees, DELTA
robot upper arm can rotates with no steady state error and no overshoot
when MPC controller is applied. But the value of an overshoot reached to
5.3% when PID controller was applied. Each of PID and MPC controllers
guaranteed no steady state error as shown is Fig. 5.29.
Chapter 6
Conclusion and Future Work
8.1 Conclusion
The purpose of this thesis was to compare the simulation results of the nonadaptive PID and MPC controllers; MPC is the most suitable control
technique to employ on the 3DOF Parallel DELTA structure. A summary of
the differences between serial and parallel robot structures introduced the
background of robotics, while the literature review provided a detailed
account of the beginning of robotics.
The 3DOF Parallel DELTA robot was introduced and modeled using
SimMechanics Matlab Tool, inverse kinematics and non-singular region in
order to adequately define the parameters of the non-linear system. The
derivations of the two controllers were solved to ensure stability of the closed
loop system. This led to the simulation of the PID and MPC controllers in
MATLAB to analyze whether the actual circular trajectory could
satisfactorily track the desired circular trajectory. Once this task was
completed, the electrical and mechanical design of the physical parallel robot
structure was discussed in detail. The two controllers attained accurate end
effector tracking results without compromising the amount of computation
time and control effort usually found in more complex control techniques. It is
highly recommended that the PID controller be utilized in various parallel
robot structures to determine if similar results can be achieved, moreover,
PID controller is easy to implement in Microcontrollers, familiar with us.
Finally, the proposed model in this thesis will open the road to master
students who wish to continue their graduate study in the field of motion
precision of the moving platforms of control systems.
8.2 Future Work
Each of PID and MPC controllers has proved the stability of the DELTA
Robot, but the design was under the assumption that Mechanical parameters
are certain, uncertainty is not considered in this thesis. The performance of
the MPC and PID controllers can be improved if we designed adaptive
I started in building of DELTA robot structure, Experimental results and
path trajectory tracking tests will be applied at real DELTA robot for
validating these results with simulation results.
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