Backstepping Fuzzy Adaptive Control Based on RBFNN for a Redundant Manipulator

Redundant manipulator is a highly nonlinear and strongly coupled system. In practical application, dynamic parameters are difficult to determine due to uncertain loads and external disturbances. These factors will adversely affect the control performance of manipulator. In view of the above problems, this paper proposes a backstepping fuzzy adaptive control algorithm based on the Radial Basis Function Neural Network (RBFNN), which effectively eliminates the influence of the internal uncertainty and external interference on the control of the manipulator. Firstly, the algorithm adopts the backstepping method to design the controller framework. Then, the fuzzy system is used to fit the unknown system dynamics represented by nonlinear function to realize model-free control of the manipulator. The fuzzy constants are optimized by RBFNN to effectively eliminate the control errors caused by unknown parameters and disturbance. Finally, in order to realize RBFNN approximating the optimal fuzzy constant, an adaptive law is designed to obtain the weight value of RBFNN. The stability of the closed-loop system is proved by using Lyapunov stability theorem. Through simulation experiments, the algorithm proposed in this paper can effectively track the target joint angle when the dynamic parameters of the 7-DOF redundant manipulator are uncertain and subject to external torque interference. Compared with fuzzy adaptive control, the tracking error of the algorithm in this paper is smaller, and the performance is better.