Neural Network Indirect Adaptive Sliding Mode Tracking Control for a Class of Nonlinear Interconnected Systems

Abstract

Based on omnipotent approximation principle, a new neural network indirect adaptive sliding mode controller is designed for a class of nonlinear interconnected systems with uncertain dynamics. Different neural networks are adopted to approximate the affection of the uncertain terms in the subsystems and the interconnected terms to the whole system. It uses the mode transformation function to realize the changing between the NN indirect adaptive controller and fuzzy sliding mode controller, which keeps the state of the system changing in a close bounded set. By using Lyapunov method, it is proved that the close-loop system is stable and the tracking errors convergence to a neighborhood of zero. The result of the emulation proofs the validation of the designed controllers.