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Observer-based adaptive neural tracking control for output-constrained switched MIMO nonstrict-feedback nonlinear systems with unknown dead zone

  • Li Ma
  • Xin Huo
  • Xudong ZhaoEmail author
  • Guang Deng Zong
Original paper

Abstract

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.

Keywords

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

Notes

Compliance with ethical standards

Conflict of interest

The authors ensure that they have no conflict of interest.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of EngineeringBohai UniversityJinzhouChina
  2. 2.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina
  3. 3.School of EngineeringQufu Normal UniversityRizhaoChina

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