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发布时间：2020-02-17

论文类型：期刊论文

发表时间：2020-01-01

发表刊物：NONLINEAR DYNAMICS

收录刊物：SCIE、EI

卷号：99

期号：2

页面范围：1019-1036

ISSN号：0924-090X

关键字：Adaptive neural control; Average dwell time (ADT); Switched MIMO nonlinear systems; Nonstrict-feedback; Backstepping; Output constraints

摘要：In this paper, the issue of adaptive neural tracking control for uncertain switched multi-input multi-output (MIMO) nonstrict-feedback nonlinear systems with average dwell time is studied. The system under consideration includes unknown dead-zone inputs and output constraints. The uncertain nonlinear functions are identified via neural networks. Also, neural networks-based switched observer is constructed to approximate all unmeasurable states. By means of the information for dead-zone slopes and barrier Lyapunov function (BLF), the problems of dead-zone inputs and output constraints are tackled. Furthermore, dynamic surface control (DSC) scheme is employed to ensure that the computation burden is greatly reduced. Then, an observer-based adaptive neural control strategy is developed on the basis of backstepping technique and multiple Lyapunov functions approach. Under the designed controller, all the signals existing in switched closed-loop system are bounded, and system outputs can track the target trajectories within small bounded errors. Finally, the feasibility of the presented control algorithm is proved via simulation results.