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Event-Triggered Adaptive Neural Control for Full State-Constrained Nonlinear Systems with Unknown Disturbances

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Abstract

This paper focuses on the adaptive control issue for a class of uncertain nonlinear systems subject to full state constraints and external disturbance. A novel adaptive nonlinear observer is proposed to compensate for disturbance variables in the transformed system. Combining with radial basis function neural networks (RBFNNs) and nonlinear mapping (NM) mechanism, the constrained system is transformed into an unconstrained form and the system uncertainties are effectively handled. Besides that, an adaptive tracking control approach is formulated by invoking backstepping techniques and the event-sampled scheme is utilized to address the sparsity of resources. The adaptive control problem can be addressed with the proposed algorithm, applying the Lyapunov functions, RBF NNs theory, and inequality techniques. Based on the Lyapunov stability theory, it is proved that the system can never violate the specified state constraints and all the closed-loop signals are semiglobally uniformly ultimately bounded (SGUUB). The validity of the proposed approach is well illustrated by a developed numerical example.

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Data Availability

All data generated or analyzed during this study are included in this published article.

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Funding

This work was supported by the National Natural and Science Foundation of China 62276214.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Westa, Southwest University, Chongqing, 400715, People’s Republic of China

    Ziming Wang, Ning Pang & Quan Shi

  2. School of Mathematical Sciences, Chongqing Normal University, Chongqing, 401331, People’s Republic of China

    Hui Wang

  3. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People’s Republic of China

    Xin Wang

  4. The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, 511400, People’s Republic of China

    Ziming Wang

Authors
  1. Ziming Wang
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  2. Hui Wang
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  3. Xin Wang
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  4. Ning Pang
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  5. Quan Shi
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Corresponding author

Correspondence to Hui Wang.

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Wang, Z., Wang, H., Wang, X. et al. Event-Triggered Adaptive Neural Control for Full State-Constrained Nonlinear Systems with Unknown Disturbances. Cogn Comput 16, 717–726 (2024). https://doi.org/10.1007/s12559-023-10223-7

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  • DOI: https://doi.org/10.1007/s12559-023-10223-7

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