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Neural-based adaptive control for nonlinear systems with quantized input and the output constraint

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  • Wu, Jing
  • Sun, Wei
  • Su, Shun-Feng
  • Xia, Jianwei

Abstract

This study reports adaptive neural network tracking control problem for a class of uncertain strict-feedback nonlinear systems with quantized input and the output constraint. To successfully overcome the obstacle caused by quantized input and the output constraint, the disintegration of hysteresis quantizer and a log-type Barrier Lyapunov function are exploited. During the control design, uncertain nonlinearities are approximated by radial basis function neural networks. Moreover, the number of adaptive law is only one, thereby reducing the computational burden. Under the proposed quantized tracking control scheme, the boundedness of all signals in the closed-loop system is validated and the output tracking error converges to an arbitrarily small domain of origin. At the same time, it can be ensured that the output constraint isn’t violated. Finally, two simulation examples are provided to verify the effectiveness of the control scheme.

Suggested Citation

  • Wu, Jing & Sun, Wei & Su, Shun-Feng & Xia, Jianwei, 2022. "Neural-based adaptive control for nonlinear systems with quantized input and the output constraint," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    • Handle: RePEc:eee:apmaco:v:413:y:2022:i:c:s0096300321007219
    DOI: 10.1016/j.amc.2021.126637
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    References listed on IDEAS

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    1. Chen, Zhongyu & Niu, Ben & Zhao, Xudong & Zhang, Liang & Xu, Ning, 2021. "Model-Based adaptive event-Triggered control of nonlinear continuous-Time systems," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    2. Khan, Wakeel & Lin, Yan & Ullah Khan, Sarmad & Ullah, Nasim, 2018. "Quantized adaptive decentralized control for interconnected nonlinear systems with actuator faults," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 175-189.
    3. Liu, Wei & Ma, Qian & Zhuang, Guangming & Lu, Junwei & Chu, Yuming, 2019. "An improved adaptive neural dynamic surface control for pure-feedback systems with full state constraints and disturbance," Applied Mathematics and Computation, Elsevier, vol. 358(C), pages 37-50.
    4. Wang, Yuanqing & Xu, Ning & Liu, Yajuan & Zhao, Xudong, 2021. "Adaptive fault-tolerant control for switched nonlinear systems based on command filter technique," Applied Mathematics and Computation, Elsevier, vol. 392(C).
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    Cited by:

    1. Liu, Yanli & Wang, Runzhi & Hao, Li-Ying, 2022. "Adaptive TD control of full-state-constrained nonlinear stochastic switched systems," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    2. Hua, Yu & Zhang, Tianping & Xia, Xiaonan, 2022. "Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    3. Cui, Di & Zou, Wencheng & Guo, Jian & Xiang, Zhengrong, 2022. "Neural network-based adaptive finite-time tracking control of switched nonlinear systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    4. Fu, Yingying & Li, Jing & Li, Xiaobo & Wu, Shuiyan, 2023. "Dynamic event-triggered adaptive control for uncertain stochastic nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    5. Zhang, Yanqi & Wang, Zhenlei & Wang, Xin, 2023. "Adaptive modified prescribed performance constraint control for uncertain nonlinear discrete-time systems," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    6. Chen, Xiang & Li, Shi & Wang, Ronghao & Xiang, Zhengrong, 2023. "Event-Triggered output feedback adaptive control for nonlinear switched interconnected systems with unknown control coefficients," Applied Mathematics and Computation, Elsevier, vol. 445(C).

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