Title of Paper:Adaptive Neural Hierarchical Sliding Mode Control of Nonstrict-Feedback Nonlinear Systems and an Application to Electronic Circuits

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Date of Publication:2017-07-01

Journal:IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

Included Journals:SCIE、EI、Scopus

Volume:47

Issue:7

Page Number:1394-1404

ISSN No.:2168-2216

Key Words:Adaptive control; hierarchical sliding mode control; neural networks

Abstract:This paper proposes an adaptive sliding mode control method for a class of nonstrict-feedback nonlinear systems where some widely used restrictions on system structure are relaxed. Based on the calculus principle, the original system is first transformed into a new defined system. Then, by using sliding mode control technology and the concept of hierarchical design, a series of control signals are sequentially designed for the new defined system where radial basis function neural networks are used to approximate the unknown functions. Based on the Lyapunov stability theory, the closed-loop system together with the proposed sliding surfaces is proved to be uniformly ultimately bounded under our designed adaptive neural controller. The main contributions of this paper lie in that some strict restrictions on uncertain system functions are removed; a hierarchical control method is proposed for the considered systems, which can avoid the problem of "causes and consequences" that may be encountered by using traditional backstepping design method; and the proposed control method is also available for underactuated nonlinear systems. Finally, simulation results demonstrate the effectiveness of the proposed design techniques.