Robot Arm Control Using Hybrid intelligent

Abstract

The design of a fuzzy logic control suffers from select parameters of the membership functions, scaling factors, defuzzification action, Inference engine and base rules. speaking generally, such prosecutions are executed by traditionally techniques which do not assure an robust fuzzy control system design. There are various techniques introduced in literatures that used Genetic Algorithms to optimize a fuzzy logic control component. . In this paper, the suggested control law consists of Fuzzy Logic Control (FLC) tuning via Geneticn Algorithm (GA). The FLC used because it is efficient tools for control of nonlinear and uncertain parameters systems. GA is mainly presented to find a simultaneous near optimum design of the membership functions, scaling factors, defuzzification Method, Inference engine and control base rules. GA with different fitness functions in a form of the cumulative response error which are widely used as an efficient optimization technique. This paper also introduce a new methodology with new multi-objective function to improve fuzzy control parameters based on Genetic Algorithm techniques. The dynamic model of the robot manipulator its done by differential equations, these equations are hardly nonlinear, parameters uncertainty and time varying with multiple input and multiple output (MIMO).The manipulator robot and the fuzzy genetic control are modeled in MATLAB SIMULINK; the manipulator robot model driven nonlinear controller to draw a circle in the space with and without parameters uncertainties. The proposed techniques showed that the proposed fuzzy controller gives superior response in the output performance. When the parameter uncertainties including in the system, given satisfactory response.