A Direct Self-adaptive Fuzzy Control System for a Single-Arm Manipulator

Abstract

In A manipulator system is a highly nonlinear and strongly coupled multi-input and multi-output complex system. This makes it difficult to obtain a precise mathematical model of the manipulator system. In addition, the external influence factors of load variation, random disturbance and model uncertainties greatly increase the difficulty of trajectory tracking control. Therefore, this paper studies manipulator trajectory tracking control and makes full use of the advantages of a simple design, does not require a precise object model, and presents an easily acceptable and understandable control mechanism and strategy via a traditional fuzzy control method. In order to compensate for low control accuracy, an adaptive fuzzy controller is proposed for single-arm manipulator control, and the stability of the controller is proved by the Lyapunov function. According to the deviation between the actual system performance and the ideal performance, the controller can directly adjust the parameters of the controller and then make the output of the system track the ideal output closely. The traditional PID controller and adaptive fuzzy controller are used to simulate and analyze the high precision trajectory tracking control of the manipulator by use of MATLAB/Simulink, and the simulation results are compared. The results show that the trajectory tracking control accuracy of the adaptive fuzzy controller is higher than that of the traditional PID controller, and the steady-state error is smaller. The robustness, anti-interference ability and response speed of the adaptive fuzzy controller are high. The validity of the algorithm is verified.