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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. |