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Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time-Varying State Constraints

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  • Lei Ma
  • Dapeng Li

Abstract

This paper proposes an adaptive neural network (NN) control approach for a direct-current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with time-varying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.

Suggested Citation

  • Lei Ma & Dapeng Li, 2018. "Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time-Varying State Constraints," Complexity, Hindawi, vol. 2018, pages 1-9, January.
    • Handle: RePEc:hin:complx:5082401
    DOI: 10.1155/2018/5082401
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    Cited by:

    1. Dapeng Wang & Shaogang Liu & Youguo He & Jie Shen, 2021. "Barrier Lyapunov Function-Based Adaptive Back-Stepping Control for Electronic Throttle Control System," Mathematics, MDPI, vol. 9(4), pages 1-14, February.

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