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Observer-Based Adaptive Fuzzy Output Feedback Control for a Class of Fractional-Order Nonlinear Systems with Full-State Constraints

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Abstract

This article focuses on adaptive fuzzy output feedback control for a class of fractional-order uncertain nonlinear strict-feedback systems with unmeasured states and full-state constraints. Fuzzy-logic systems are employed to approximate uncertain nonlinear functions, and a fractional-order fuzzy state observer based on the structure of the considered systems is framed to estimate the unmeasurable states. In each step of backstepping procedure, a barrier Lyapunov function is introduced in the design of the controller and the adaptation laws to satisfy the condition of the state constraints. Based on the fractional-order Lyapunov stability theory, a fractional-order adaptive fuzzy controller is constructed to guarantee that all the states remain in their constraint bounds, the tracking error converges to a bounded compact set containing the origin, and all signals in the closed-loop system are ensured to be bounded. Finally, a simulation example verifies the effectiveness of the proposed control design.

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Acknowledgements

This paper was supported in part by the Doctoral Program of Shandong Provincial Natural Science Foundation of China (ZR2019BF048), Shandong Provincial Natural Science Foundation of China (ZR2019MEE093), and Yantai Science and Technology Innovation Development Project (2020XDRH094,2021XDHZ077).

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Authors and Affiliations

  1. School of Electromechanical and Automotive Engineering, Yantai University, 32 Qingquan Road, Laishan District, Yantai, People’s Republic of China

    Mei Liang, Yaowen Chang, Fuyi Zhang, Shouli Wang, Changhui Wang, Shuai Lu & Yantao Wang

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  1. Mei Liang
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  2. Yaowen Chang
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  3. Fuyi Zhang
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  4. Shouli Wang
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  5. Changhui Wang
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Correspondence to Changhui Wang or Shuai Lu.

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Liang, M., Chang, Y., Zhang, F. et al. Observer-Based Adaptive Fuzzy Output Feedback Control for a Class of Fractional-Order Nonlinear Systems with Full-State Constraints. Int. J. Fuzzy Syst. 24, 1046–1058 (2022). https://doi.org/10.1007/s40815-021-01189-5

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