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Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Full-State Constraints

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Abstract

This study focuses on addressing the challenge of adaptive fuzzy tracking control for stochastic nonlinear systems while considering full-state constraints. To tackle this issue, we introduce a fuzzy logic system to approximate the unknown nonlinear terms in the system. Additionally, a barrier Lyapunov function is utilized to confine the system state within a specific range. In order to design a novel tracking controller, the backstepping design method and the fuzzy control approach are combined. To optimize system performance, a hysteresis quantizer is integrated. To evaluate the effectiveness of the proposed control strategy, two simulation examples are presented. The simulation results demonstrate that the proposed control strategy effectively limits all state variables to a predefined range. Additionally, the tracking error of the system steadily converges to a negligible range near zero.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62372104, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515110518, in part by the Shenzhen Science and Technology Program under Grant JCYJ20210324121213036. Besides, we thank the Big Data Computing Center of Southeast University for providing the facility support on the numerical calculations.

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Authors and Affiliations

  1. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing, 210096, China

    Yefeng Xu, Yihao Zhang, Sijia Chen, Kanjian Zhang & Liping Xie

  2. Southeast University Shenzhen Research Institute, Shenzhen, 518063, China

    Kanjian Zhang & Liping Xie

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  1. Yefeng Xu
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Correspondence to Liping Xie.

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Xu, Y., Zhang, Y., Chen, S. et al. Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Full-State Constraints. Int. J. Fuzzy Syst. 26, 1840–1851 (2024). https://doi.org/10.1007/s40815-024-01706-2

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