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Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays

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  • Zhou, Xingyu
  • Tian, Yang
  • Wang, Haoping

Abstract

In this work, the neural network state observer-based robust adaptive quantized iterative learning output feedback control (RAQILOFC) is investigated for rigid-flexible coupled robotic systems (RFCRSs) with unknown time delays and actuator faults. To deal with hysteresis quantization and actuator defects, a novel fault-tolerant RAQILOFC is designed first, based only on accessible system output data. Then, using the fault-tolerant RAQILOFC laws in combination with the neural network state observer, the two given angular positions are tracked while concurrently suppressing the flexible vibration. Simultaneously, uncertainties associated with system dynamics and unknown time delays are taken into account while designing controllers for RFCRSs. It is demonstrated that the fault-tolerant RAQILOFC technique would converge to and remain inside a predefined small compact set after a finite number of cycles. Additionally, it is shown that the system signal sequences are bounded in presence of unknown time delays and hysteresis quantization. Finally, a numerical example is conducted to illustrate the proposed neural network state observer-based fault-tolerant RAQILOFC strategy’s efficacy.

Suggested Citation

  • Zhou, Xingyu & Tian, Yang & Wang, Haoping, 2022. "Neural network state observer-based robust adaptive fault-tolerant quantized iterative learning control for the rigid-flexible coupled robotic systems with unknown time delays," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    • Handle: RePEc:eee:apmaco:v:430:y:2022:i:c:s0096300322003605
    DOI: 10.1016/j.amc.2022.127286
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    References listed on IDEAS

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    1. Xie, Dan & Jian, Kailin & Wen, Weibin, 2017. "An element-free Galerkin approach for rigid–flexible coupling dynamics in 2D state," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 149-168.
    2. Xu, Xiaofeng & Chen, Mou & Li, Tao & Wu, Qingxian, 2021. "Composite fault tolerant attitude control for flexible satellite system under disturbance and input delay," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    3. Zhao, Huarong & Peng, Li & Yu, Hongnian, 2022. "Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 412(C).
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    Cited by:

    1. Qi, Yiwen & Qu, Ziyu & Yao, Zhaohui & Zhao, Xiujuan & Tang, Yiwen, 2023. "Event-Triggered iterative learning control for asynchronously switched systems," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    2. Chen, Wu-Hua & Sun, Hao & Lu, Xiaomei, 2024. "A variable gain impulsive observer for perturbed Lipschitz nonlinear systems with delayed discrete measurements," Applied Mathematics and Computation, Elsevier, vol. 473(C).
    3. Zhang, Jianan & Ma, Yuechao, 2023. "Adaptive fault-tolerant double asynchronous control for switched semi-Markov jump systems via improved memory sampled-data technique," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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