Robot Arm Control Using Fuzzy Logic Design Integrated With Genetic Algorithm Optimization

Abstract

Abstract— The main contribution of this proposed study is to control the robot's arm angle by changing the torque of the each joints .The dynamic model of the robot manipulator contain from equations, these equations are coupled differential equations , hardy nonlinear, highly complex, multiple inputs and multiple outputs (MIMO), coupled differential equations ,strong model uncertainties and time-variant . The conventional computed torque controllers are not suitable for nonlinear systems, complex, time-variant systems with delay. In this paper, the suggested control law consists of Fuzzy Logic Control (FLC) tuning via Genetic Algorithm (GA). The FLC used, because it is efficient tools for control of nonlinear and uncertain parameters systems. In this design, GA is mainly presented to find a simultaneous near optimum design of the membership functions, scaling factors ,defuzzification Method ,Inference enginee and control rules. GA with a fitness function in a form of a cumulative response error which is widely utilized as an efficient optimization technique. This paper proposed a methodology to optimize fuzzy logic parameters based on GA. The whole system is simulated using MATLAB Simulink. Through this study it is proved that the optimized fuzzy controller gives near optimum performance in the time response behavior ,When the uncertainty added to the system, it maintained roughly the same level of controller performance .