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论文类型： 期刊论文

发表时间： 2017-07-01

发表刊物： IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录刊物： SCIE、EI、Scopus

卷号： 47

期号： 7

页面范围： 1394-1404

ISSN号： 2168-2216

关键字： Adaptive control; hierarchical sliding mode control; neural networks

摘要： 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.