언바운드의 비구조적 불확실성을 갖는 비선형 시스템을 위한 신경회로망기반의 적응제어기법

Abstract

This paper presents a neural network adaptive control scheme for a class of nonlinear systems with unknown -bound unstructured uncertainties. Here, ‘unknown-bound’ denotes certain growth condition characterized by ‘bounding function’ composed of known function multiplied by unknown constant. All adaptation laws for unknown bounds of unstructured uncertainties are derived from Lyapunov-based method as well as the adaptation laws for the networks’ weights values. In addition, the unknown control gain functions are not approximated directly by neural networks. Therefore, we can avoid the possible controller singularity problems. Under a certain relaxed assumptions on the control gain functions, proposed control scheme can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of presented control scheme, and some practical features of the control laws are also discussed.