On-line genetic algorithm-based fuzzy-neural sliding mode controller using improved adaptive bound reduced-form genetic algorithm

Abstract

In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.