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Adaptive Fuzzy Output-Feedback Tracking Control for Nonlinear Time-Varying State-Constrained Systems Without the Feasibility Condition

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Abstract

This paper investigates an adaptive fuzzy tracking controller for uncertain strict-feedback nonlinear systems suffering from asymmetric time-varying state constraints, unmeasured states, and unknown external disturbances. Firstly, a state observer with a simplified structure is constructed to approximate the unavailable system states based on fuzzy logic systems. The difficulties caused by time-varying state constraints are effectively addressed in a direct approach via the nonlinear state-dependent functions. Compared to the classical barrier Lyapunov functions-based (BLFs-based) method, the strict condition imposed on the virtual controller is eliminated, which significantly enhances the applicability of the controller in practical systems. Meanwhile, to handle the issue of “explosion of complexity,” a novel adaptive fuzzy tracking control technique is proposed by using the sliding-mode differentiator. Finally, rigorous stability analysis is employed to verify that the error between the system output signal and the reference signal can converge to an arbitrarily small set around the origin, and all closed-loop signals are bounded without time-varying state constraints violation. The effectiveness of the theoretical result is further illustrated by two simulation examples.

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References

  1. Cui, D., Xiang, Z.R.: Nonsingular fixed-time fault-tolerant fuzzy control for switched uncertain nonlinear systems. IEEE Trans. Fuzzy Syst. 31(1), 174–183 (2023)

    Article  MathSciNet  Google Scholar 

  2. Li, Y.M., Yang, T.T., Tong, S.C.: Adaptive neural networks finite-time optimal control for a class of nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 31(11), 4451–4460 (2020)

    Article  MathSciNet  PubMed  Google Scholar 

  3. Su, H., Zhang, W.H.: Observer-based adaptive neural quantized control for nonlinear systems with asymmetric fuzzy dead zones and unknown control directions. Nonlinear Dyn. 108(4), 3643–3656 (2022)

    Article  Google Scholar 

  4. Cui, D., Zou, W.C., Guo, J., Xiang, Z.R.: Neural network-based adaptive finite-time tracking control of switched nonlinear systems with time-varying delay. Appl. Math. Comput. 428, 89 (2022). https://doi.org/10.1016/j.amc.2022.127216

    Article  MathSciNet  Google Scholar 

  5. Liu, H., Pan, Y.P., Cao, J.D.: Composite learning adaptive dynamic surface control of fractional-order nonlinear systems. IEEE Trans. Cybern. 50(6), 2557–2567 (2020)

    Article  PubMed  Google Scholar 

  6. Wu, J., Chen, X.M., Zhao, Q.J., Li, J., Wu, Z.G.: Adaptive neural dynamic surface control with prespecified tracking accuracy of uncertain stochastic nonstrict-feedback systems. IEEE Trans. Cybern. 525, 3408–3421 (2022)

    Article  Google Scholar 

  7. Xia, J.W., Zhang, J., Feng, J., Wang, Z., Zhuang, G.M.: Command filter-based adaptive fuzzy control for nonlinear systems with unknown control directions. IEEE Trans. Syst. Man Cybern. 51(3), 1945–1953 (2021)

    Google Scholar 

  8. Chen, M., Li, Y.S., Wang, H.Q., Peng, K.X., Wu, L.B.: Adaptive fixed-time tracking control for nonlinear systems based on finite-time command-filtered backstepping. IEEE Trans. Fuzzy Syst. 31(5), 1604–1613 (2023)

    Article  Google Scholar 

  9. Ni, J.K., Shi, P.: Global predefined time and accuracy adaptive neural network control for uncertain strict-feedback systems with output constraint and dead zone. IEEE Trans. Syst. Man Cybern. 51(12), 7903–7918 (2021)

    Article  Google Scholar 

  10. Su, H., Qi, W., Chen, J.H., Zhang, D.D.: Fuzzy approximation-based task-space control of robot manipulators with remote center of motion constraint. IEEE Trans. Fuzzy Syst. 30(6), 1564–1573 (2022)

    Article  Google Scholar 

  11. Tee, K.P., Ge, S.S., Tay, E.H.: Barrier Lyapunov functions for the control of output-constrained nonlinear systems. Automatica 45(4), 918–927 (2009)

    Article  MathSciNet  Google Scholar 

  12. Wang, L.J., Chen, C.L.P., Li, H.Y.: Event-triggered adaptive control of saturated nonlinear systems with time-varying partial state constraints. IEEE Trans. Cybern. 50(4), 1485–1497 (2020)

    Article  PubMed  Google Scholar 

  13. Sun, W., Su, S.F., Wu, Y.Q., Xia, J.W., Nguyen, V.T.: Adaptive fuzzy control with high-order barrier Lyapunov functions for high-order uncertain nonlinear systems with full-state constraints. IEEE Trans. Cybern. 50(8), 3424–3432 (2020)

    Article  PubMed  Google Scholar 

  14. Zhou, S.H., Sui, S., Cheng, T.S.: Adaptive neural networks optimal control of permanent magnet synchronous motor system with state constraints. Neurocomputing 504, 132–140 (2022)

    Article  Google Scholar 

  15. Sun, W., Su, S.F., Dong, G.W., Bai, W.W.: Reduced adaptive fuzzy tracking control for high-order stochastic nonstrict feedback nonlinear system with full-state constraints. IEEE Trans. Syst. Man Cybern. 51(3), 1496–1506 (2021)

    Google Scholar 

  16. Wang, N., Tao, F.Z., Fu, Z.M., Song, S.Z.: Adaptive fuzzy control for a class of stochastic strict feedback high-order nonlinear systems with full-state constraints. IEEE Trans. Syst. Man Cybern. 52(1), 205–213 (2022)

    Article  CAS  Google Scholar 

  17. Fang, L.D., Ma, L., Ding, S.H., Park, J.H.: Finite-time stabilization of high-order stochastic nonlinear systems with asymmetric output constraints. IEEE Trans. Syst. Man Cybern. 51(11), 7201–7213 (2021)

    Article  Google Scholar 

  18. Wei, W., Zhang, T.L., Zhang, W.H.: Event-triggered finite-time adaptive practical tracking control for time-varying state-constrained nonlinear systems. IEEE Trans. Fuzzy Syst. 31(5), 1580–1592 (2023)

    Article  Google Scholar 

  19. Zhao, K., Song, Y.D., Zhang, Z.R.: Tracking control of MIMO nonlinear systems under full state constraints: a single-parameter adaptation approach free from feasibility conditions. Automatica 107, 52–60 (2019)

    Article  MathSciNet  Google Scholar 

  20. Niu, B., Zhang, Y., Zhao, X. D., Wang, H. Q., Sun, W.: Adaptive predefined-time bipartite consensus tracking control of constrained nonlinear MASs: an improved nonlinear mapping function method. IEEE Trans. Cybern, early access. https://doi.org/10.1109/TCYB.2022.3231900

  21. Xie, X.J., Wu, Y., Hou, Z.G.: Further results on adaptive practical tracking for high-order nonlinear systems with full-state constraints. IEEE Trans. Cybern. 52(10), 9978–9985 (2022)

    Article  PubMed  Google Scholar 

  22. Zhao, K., Song, Y.D.: Removing the feasibility conditions imposed on tracking control designs for state-constrained strict-feedback systems. IEEE Trans. Autom. Control 64(3), 1265–1272 (2019)

    Article  MathSciNet  Google Scholar 

  23. Islam, S., Liu, P.X., Saddik, A.E.: Observer-based adaptive output feedback control for miniature aerial vehicle. IEEE Trans. Ind. Electron. 65(1), 470–477 (2018)

    Article  Google Scholar 

  24. Wei, W., Zhang, W.H.: Command-filter-based adaptive fuzzy finite-time output feedback control for state-constrained nonlinear systems with input saturation. IEEE Trans. Fuzzy Syst. 30(10), 4044–4056 (2022)

    Article  Google Scholar 

  25. Li, Y.M., Tong, S.C., Liu, Y.J., Li, T.S.: Adaptive fuzzy robust output feedback control of nonlinear systems with unknown dead zones based on a small-gain approach. IEEE Trans. Fuzzy Syst. 22(1), 164–176 (2014)

    Article  Google Scholar 

  26. Zhang, Z.R., Wen, C.Y., Xing, L.T., Song, Y.D.: Adaptive output feedback control of nonlinear systems with mismatched uncertainties under input/output quantization. IEEE Trans. Autom. Control 67(9), 4801–4808 (2022)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant Nos. 62373229 and 61973198, and China Scholarship Council under Grant No. CSC202208370123.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Xiaoning Lv, Wei Wei & Weihai Zhang

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  1. Xiaoning Lv
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  2. Wei Wei
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  3. Weihai Zhang
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Correspondence to Weihai Zhang.

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Lv, X., Wei, W. & Zhang, W. Adaptive Fuzzy Output-Feedback Tracking Control for Nonlinear Time-Varying State-Constrained Systems Without the Feasibility Condition. Int. J. Fuzzy Syst. (2024). https://doi.org/10.1007/s40815-023-01663-2

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  • DOI: https://doi.org/10.1007/s40815-023-01663-2

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