الفهرس 
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Abstract
Dynamic control of robot manipulators is one of the most important topics in
robotics. Various modem control strategies have been widely investigated to deal with
the high nonlinearity and strong coupling of the robot dynamics. Generally,
controllers are designed assuming an exact knowledge about the model structure and
do not include uncertainties in robot systems.
This thesis introduces a decentralized fuzzy control system of robot
manipulators consisting of two controllers: a feedforward fuzzy torque-computing
system and a feedback fuzzy PD controller. The aim is building a position controller
for robot manipulators, which not only exhibits strong robustness in the presence of
various uncertainties, but also is computationally very efficient.
The feedforward fuzzy system satisfies two main characteristics:
• The capability of setting up optimal fuzzy rules .
• The ability to adjust the rule parameters.
The feedforward system assumes a Mamdani fuzzy model. In the proposed system,
Mamdani model’s output membership functions are assumed first to be uniformly
distributed along the output range, then QA is applied to fit the best positions of these
membership functions. The obtained results show better performance.
On the other hand, the feedback system assumes a fuzzy PD controller built
using T -S model. The output torque is taken as a linear combination of the two inputs
(position and velocity) which satisfies a fuzzy T-S model without approximationsSimilar to the feedforward case, the genetic algorithm is applied to adjust the
controller parameters for each rule. Due to robot manipulators various applications,
uncertainties such as friction, parameter variation, and unknown payloads may occur.
The main task of the feedback system is to compensate against all these problems.
The robustness of the system is tested using various computer simulations; the system
performance is tested for various errors, positive or negative, large or small. The
obtained results show the stability of the system.
The present thesis consists of five chapters:
Chapter 1 is an introduction to fuzzy sets, fuzzification, fuzzy rule- based
. systems, and genetic algorithm (GA).
Chapter 2 gives a survey for the previous work done that used fuzzy as an easy
way of control. Also, it describes an effective fuzzy- genetic system built for
controlling robot manipulators. Advantages and disadvantages of this method are
mentioned.
Chapter 3 introduces the proposed feedforward fuzzy system. Many trials are
shown till the best system is selected. Optimization of the system by using GA is
explained in full details.
Chapter 4 introduces the feedback fuzzy proportional- derivative (PD) controller
used to satisfy the system stability and compensate against the uncertainties that
may occur. Descriptions of the proposed fuzzy system and simulation results are
shown by the aid of curves and data.
Chapter 5 concludes the wor¥ done in this thesis, and shows how the proposed
methods are effective to simulate this type of manipulators. Future work is also
mentioned to get better performance in a few specific points.