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Pattern-based learning and control of nonlinear pure-feedback systems with prescribed performance

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Abstract

This article presents a novel pattern-based intelligent control scheme with prescribed performance (PP) for uncertain pure-feedback systems operating in multiple control situations (patterns). Based on PP, an observer-based adaptive neural network (NN) control approach, which not only achieves system stability and prescribed tracking control performance but also realizes accurate identification/learning of the unknown closed-loop dynamics via deterministic learning and uses only one NN unit, is proposed. Subsequently, the knowledge learned is utilized to construct high-performance candidate controllers for each control situation. Based on the transformed system and observer technique, accurate classification of the nth order systems under different control situations is achieved by requiring only one set of dynamic estimators, thereby significantly reducing the complexity of pattern recognition. Thus, sudden changes in the control situation can be rapidly recognized based on the minimum residual principle, with which the correct candidate controller is selected to achieve superior control performance. The simulation results verify the efficacy of the proposed scheme.

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Acknowledgements

This work was supported by Major Program of the National Natural Science Foundation of China (Grant No. 61890922) and Major Basic Program of Shandong Provincial Natural Science Foundation, China (Grant No. ZR2020ZD40).

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Authors and Affiliations

  1. Center for Intelligent Medical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Fukai Zhang, Weiming Wu & Cong Wang

Authors
  1. Fukai Zhang
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  2. Weiming Wu
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  3. Cong Wang
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Correspondence to Cong Wang.

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Zhang, F., Wu, W. & Wang, C. Pattern-based learning and control of nonlinear pure-feedback systems with prescribed performance. Sci. China Inf. Sci. 66, 112202 (2023). https://doi.org/10.1007/s11432-021-3434-9

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  • DOI: https://doi.org/10.1007/s11432-021-3434-9

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