A Robust Adaptive CMAC Neural Network-Based Multisliding Mode Control Method for Unmatched Uncertain Nonlinear Systems

Honghui Wang,Zeming Fan,Xiaojun Yu,Sheng Dong,Shicheng Liang,Zhaohui Yuan

doi:10.1155/2020/1615345

Abstract

This paper proposes a new robust adaptive cerebellar model articulation controller (CMAC) neural network-based multisliding mode control strategy for a class of unmatched uncertain nonlinear systems. Specifically, by employing a stepwise recursion-based multisliding mode method, such a proposed strategy is able to obtain the virtual variables and the actual control inputs of each order first, and then it reduces the conservativeness for controller parameter design by adopting the CMAC neural network to learn both system uncertainties and virtual control variable derivatives of each order online. Meanwhile, with the hyperbolic tangent function being chosen to replace the sign function in the variable structured control components, the proposed strategy is able to avoid the chattering effects caused by the discontinuous inputs. The stability analysis shows that the proposed control strategy ensures that both the system tracking errors and the sliding modes of each order could converge exponentially to any saturated layer being set. The control strategy was also applied onto a passive electrohydraulic servo loading system for verifications, and simulation results show that such a proposed control strategy is robust against all system nonlinearities and external disturbances with much higher control accuracy being achieved.

Highlights

With the continuous improvement and widespread applications of automation systems, the performance requirements for the control systems in various industrial fields are increasing dramatically, and those systems with poor convergence are difficult to meet the practical application requirements
Is paper presents a new robust adaptive sliding mode control method, which guarantees that the sliding modes and the output tracking errors converge exponentially to an arbitrarily set saturated layer for a class of unmatched uncertain nonlinear systems. e proposed method can effectively address the problem that a larger sliding mode of each order leads to a larger virtual control and actual control quantities
Discussion and Conclusion is paper studies the robust output tracking problem for a class of unmatched uncertain nonlinear systems, and a new robust adaptive control method is proposed based on the combination of multisliding mode and the backstepping stepwise recursion mechanism

Summary

Introduction

With the continuous improvement and widespread applications of automation systems, the performance requirements for the control systems in various industrial fields are increasing dramatically, and those systems with poor convergence are difficult to meet the practical application requirements. It is worth noting that all those proposed schemes considered only the infinite-time stability problems, wherein the tracking errors converge only if the time is infinity To further address such practical issues, a number of event- or observer-based adaptive finite-time tracking control strategies have been proposed recently [21,22,23,24,25,26,27]. Is paper presents a new robust adaptive sliding mode control method, which guarantees that the sliding modes and the output tracking errors converge exponentially to an arbitrarily set saturated layer for a class of unmatched uncertain nonlinear systems.

Problem Description

Robust Multisliding Mode Controller Design

Findings

Stability Analysis