Online Adaptive Neural Observer for Prescribed Performance Hyper-Chaotic Systems

Abstract

This paper proposes a new neural-based algorithm applied to online identifying, observing, and adaptively controlling of uncertain nonlinear systems via which the system transient and residual values independently regulated. The Lyapunov stability concept is used for enhancing the control performance. Especially, distinguished from recent related studies, not only identified model but the learning method and control rules are also innovatively implemented through which it independently separates the transient-time regulation from residual error adjustment.Sequentially, an advanced adaptive neural controller for an uncertain nonlinear plant with state feedback is implemented using the open-loop estimation result. The key contribution of the online estimator will be verified in this proposed control algorithm. Eventually, as to verify the prescribed performance and the flexibility of the suggested control approach, the estimation and control of an uncertain hyper-chaotic weld plant will be tested.