Observer-based adaptive FNN controller optimized by NAPSOSA for nonlinear time-delay systems

Abstract

This study presents an observer-based adaptive fuzzy-neural network controller based on novel adaptive particle swarm optimization simulated annealing (NAPSOSA) for a class of uncertain nonlinear time-delay systems. Firstly, NAPSOSA is used to adjust the weighting function. Then, adaptive laws are adopted to approximate unknown nonlinear functions with unknown uncertainties, respectively. By examining the controller design, the observer-based control law and the weighting update law of the fuzzy-neural-network (FNN) controller are proposed for a class of nonlinear systems. In addition, based on strictly-positive-real (SPR) Lyapunov theory, the stabilization conditions for the closed-loop system are propounded. Furthermore, for obtaining a better performance, an algorithm consists of the adaptive FNN with NAPSOSA is presented to adjust the membership function of the controller. Finally, one simulation example is given to illustrate the effectiveness of the proposed approach.