Fuzzy neural sliding mode control based on genetic algorithm for multi-link robots

Abstract

A fuzzy neural sliding mode controller based on genetic algorithm (FNSMCGA) is presented for trajectory tracking control of multi-link robots with model errors and uncertain disturbances. This approach gives a new global sliding mode manifold for multi-link robots, which enable system trajectory to run on the sliding mode manifold at the start point and eliminate the reaching phase of the conventional sliding mode control. Robustness for system dynamics is guaranteed over all the response time. A fuzzy neural network (FNN) is employed to eliminate chattering of global sliding mode control, and enforce the sliding mode motion by FNN learning the upper bound of model errors and uncertain disturbances. Genetic algorithm can optimize the FNN initial parameters, which can make the robot running with expected trajectory in whole running process. The control laws are calculated by Lyapunov stability method, which ensure that the controlled system is stable. Simulation results verify the validity of the control scheme.