张硕

个人信息Personal Information

副教授

硕士生导师

性别:男

毕业院校:德国柏林工业大学

学位:博士

所在单位:控制科学与工程学院

电子邮箱:shuozhang@dlut.edu.cn

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Adaptive Neural Backstepping Control Design for A Class of Nonsmooth Nonlinear Systems

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论文类型:期刊论文

发表时间:2019-09-01

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

收录刊物:EI、SCIE

卷号:49

期号:9

页面范围:1820-1831

ISSN号:2168-2216

关键字:Adaptive control; backstepping; neural networks (NNs); nonsmooth nonlinear systems

摘要:This paper investigates the problems of backstepping control for a class of nonsmooth nonlinear systems. With the help of set-valued functions (or maps) and set-valued derivatives, a new Lyapunov criterion is developed for nonsmooth systems. The concept of semi-globally uniformly ultimately bounded (SGUUB) stability that is widely used for smooth nonlinear systems in lower triangular form is, for the first time, applied to the nonsmooth case, and such a concept provides a theory foundation for the subsequent backstepping control design. Then, two types of continuous controllers are designed based on the backstepping technique and adaptive neural network (NN) algorithm. In the light of Cellina approximate selection theorem and smooth approximation theorem for Lipschitz functions, the system under investigation is first transformed into an equivalent model. Then, the first type of controller can be efficiently designed by using the traditional backstepping technique. On the other hand, when the unknown uncertainty is taken into account in the equivalent systems, another type of controller is designed based on adaptive NN control strategy. It is proved that in both cases, all the closed-loop signals are SGUUB by using our proposed controllers. Finally, a numerical example with two types of controllers is supplied to show the availability of the provided control schemes.