150
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

Dynamic event-triggered adaptive fuzzy control for stochastic nontriangular nonlinear systems with unknown backlash-like hysteresis

, , & ORCID Icon
Pages 1550-1562 | Received 21 Nov 2022, Accepted 11 Feb 2023, Published online: 28 Feb 2023

References

  • Abd-Elhaleem, S., Soliman, M., & Hamdy, M. (2022a). Modified repetitive periodic event-triggered control with equivalent-input-disturbance for linear systems subject to unknown disturbance. International Journal of Control, 95(7), 1825–1837. https://doi.org/10.1080/00207179.2021.1876924
  • Abd-Elhaleem, S., Soliman, M., & Hamdy, M. (2022b). Periodic event-triggered modified repetitive control with equivalent-input-disturbance estimator based on TS fuzzy model for nonlinear systems. Soft Computing, 26(13), 6443–6459. https://doi.org/10.1007/s00500-022-06973-5
  • Abd-Elhaleem, S., Soliman, M., & Hamdy, M. (2023). Design of equivalent-input-disturbance estimator based modified repetitive control with adaptive periodic event-triggered for time-varying delay nonlinear systems. International Journal of Robust and Nonlinear Control, 33(3), 1894–1913. https://doi.org/10.1002/rnc.6501
  • Bechlioulis, C. P., & Rovithakis, G. A. (2008). Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance. IEEE Transactions on Automatic Control, 53(9), 2090–2099. https://doi.org/10.1109/TAC.2008.929402
  • Bhat, S. P., & Bernstein, D. S. (1998). Continuous finite-time stabilization of the translational and rotational double integrators. IEEE Transactions on Automatic Control, 43(5), 678–682. https://doi.org/10.1109/9.668834
  • Bhat, S. P., & Bernstein, D. S. (2000). Finite-time stability of continuous autonomous systems. SIAM Journal on Control and Optimization, 38(3), 751–766. https://doi.org/10.1137/S0363012997321358
  • Fu, C., Wang, Q. G., Yu, J., & Lin, C. (2020). Neural network-based finite-time command filtering control for switched nonlinear systems with backlash-like hysteresis. IEEE Transactions on Neural Networks and Learning Systems, 32(7), 3268–3273. https://doi.org/10.1109/TNNLS.2020.3009871
  • Fu, Z., Wang, N., Song, S., & Wang, T. (2020). Adaptive fuzzy finite-time tracking control of stochastic high-order nonlinear systems with a class of prescribed performance. IEEE Transactions on Fuzzy Systems, 30(1), 88–96. https://doi.org/10.1109/TFUZZ.2020.3032776
  • Ge, S. S., Li, G. Y., & Lee, T. H. (2003). Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems. Automatica, 39(5), 807–819. https://doi.org/10.1016/S0005-1098(03)00032-3
  • Girard, A. (2014). Dynamic triggering mechanisms for event-triggered control. IEEE Transactions on Automatic Control, 60(7), 1992–1997. https://doi.org/10.1109/TAC.9
  • Hua, C., Meng, R., Li, K., & Ning, P. (2022). Dynamic event-based adaptive finite-time tracking control for nonlinear stochastic systems under state constraints. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(11), 7201–7210. https://doi.org/10.1109/TSMC.2022.3151669
  • Li, B., Xia, J., Zhang, H., Shen, H., & Wang, Z. (2020). Event-triggered adaptive fuzzy tracking control for stochastic nonlinear systems. Journal of the Franklin Institute, 357(14), 9505–9522. https://doi.org/10.1016/j.jfranklin.2020.07.023
  • Li, H., Bai, L., Wang, L., Zhou, Q., & Wang, H. (2016). Adaptive neural control of uncertain nonstrict-feedback stochastic nonlinear systems with output constraint and unknown dead zone. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(8), 2048–2059. https://doi.org/10.1109/TSMC.2016.2605706
  • Li, Y., Li, K., & Tong, S. (2019). Adaptive neural network finite-time control for multi-input and multi-output nonlinear systems with positive powers of odd rational numbers. IEEE Transactions on Neural Networks and Learning Systems, 31(7), 2532–2543. https://doi.org/10.1109/TNNLS.2019.2933409
  • Li, Y., Shao, X., & Tong, S. (2019). Adaptive fuzzy prescribed performance control of nontriangular structure nonlinear systems. IEEE Transactions on Fuzzy Systems, 28(10), 2416–2426. https://doi.org/10.1109/TFUZZ.91
  • Li, Y., & Tong, S. (2016). Adaptive fuzzy output constrained control design for multi-input multioutput stochastic nonstrict-feedback nonlinear systems. IEEE Transactions on Cybernetics, 47(12), 4086–4095. https://doi.org/10.1109/TCYB.2016.2600263
  • Li, Y., Zhang, J., Liu, W., & Tong, S. (2021). Observer-based adaptive optimized control for stochastic nonlinear systems with input and state constraints. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 7791–7805. https://doi.org/10.1109/TNNLS.2021.3087796.
  • Liu, Y., Liu, X., & Jing, Y. (2018). Adaptive neural networks finite-time tracking control for non-strict feedback systems via prescribed performance. Information Sciences, 468, 29–46. https://doi.org/10.1016/j.ins.2018.08.029.
  • Liu, Z., Wang, F., Zhang, Y., & Chen, C. P. (2015). Fuzzy adaptive quantized control for a class of stochastic nonlinear uncertain systems. IEEE Transactions on Cybernetics, 46(2), 524–534. https://doi.org/10.1109/TCYB.2015.2405616
  • Ma, H., Li, H., Liang, H., & Dong, G. (2019). Adaptive fuzzy event-triggered control for stochastic nonlinear systems with full state constraints and actuator faults. IEEE Transactions on Fuzzy Systems, 27(11), 2242–2254. https://doi.org/10.1109/TFUZZ.91
  • Ma, Y. S., Che, W. W., & Deng, C. (2022). Dynamic event-triggered model-free adaptive control for nonlinear CPSs under aperiodic DoS attacks. Information Sciences, 589, 790–801. https://doi.org/10.1016/j.ins.2022.01.009.
  • Min, H., Xu, S., Yu, X., Fei, S., & Cui, G. (2020). Adaptive tracking control for stochastic nonlinear systems with full-state constraints and unknown covariance noise. Applied Mathematics and Computation, 385(C), Article 125397. https://doi.org/10.1016/j.amc.2020.125397.
  • Polyakov, A., Efimov, D., & Perruquetti, W. (2015). Finite-time and fixed-time stabilization: Implicit Lyapunov function approach. Automatica, 51, 332–340. https://doi.org/10.1016/j.automatica.2014.10.082
  • Sun, Y., Chen, B., Lin, C., Wang, H., & Zhou, S. (2016). Adaptive neural control for a class of stochastic nonlinear systems by backstepping approach. Information Sciences, 369(C), 748–764. https://doi.org/10.1016/j.ins.2016.06.010.
  • Tong, S., Wang, T., & Li, Y. (2013). Fuzzy adaptive actuator failure compensation control of uncertain stochastic nonlinear systems with unmodeled dynamics. IEEE Transactions on Fuzzy Systems, 22(3), 563–574. https://doi.org/10.1109/TFUZZ.91
  • Wang, F., Chen, B., Zhang, Z., & Lin, C. (2016). Adaptive tracking control of uncertain switched stochastic nonlinear systems. Nonlinear Dynamics, 84(4), 2099–2109. https://doi.org/10.1007/s11071-016-2631-6
  • Wang, H., Chen, B., Liu, K., Liu, X., & Lin, C. (2013). Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 947–958. https://doi.org/10.1109/TNNLS.2013.2283879
  • Wang, T., Ma, M., Qiu, J., & Gao, H. (2020). Event-triggered adaptive fuzzy tracking control for pure-feedback stochastic nonlinear systems with multiple constraints. IEEE Transactions on Fuzzy Systems, 29(6), 1496–1506. https://doi.org/10.1109/TFUZZ.2020.2979668
  • Wang, X., Cao, Y., Niu, B., & Song, Y. (2022). A novel bipartite consensus tracking control for multiagent systems under sensor deception attacks. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2022.3225361
  • Wang, Y., Zheng, W. X., & Zhang, H. (2017). Dynamic event-based control of nonlinear stochastic systems. IEEE Transactions on Automatic Control, 62(12), 6544–6551. https://doi.org/10.1109/TAC.2017.2707520
  • Xia, J., Li, B., Su, S. F., Sun, W., & Shen, H. (2020). Finite-time command filtered event-triggered adaptive fuzzy tracking control for stochastic nonlinear systems. IEEE Transactions on Fuzzy Systems, 29(7), 1815–1825. https://doi.org/10.1109/TFUZZ.2020.2985638
  • Yao, Y., Tan, J., Wu, J., & Zhang, X. (2021a). Event-triggered fixed-time adaptive neural dynamic surface control for stochastic non-triangular structure nonlinear systems. Information Sciences, 569, 527–543. https://doi.org/10.1016/j.ins.2021.05.028
  • Yao, Y., Tan, J., Wu, J., & Zhang, X. (2021b). Event-triggered fixed-time adaptive neural tracking control for stochastic non-triangular structure nonlinear systems. Neural Computing and Applications, 33(22), 15887–15899. https://doi.org/10.1007/s00521-021-06210-4
  • Yao, Y., Tan, J., Wu, J., & Zhang, X. (2022). A unified fuzzy control approach for stochastic high-Order nonlinear systems with or without state constraints. IEEE Transactions on Fuzzy Systems, 30(10), 4530–4540. https://doi.org/10.1109/TFUZZ.2022.3155297.
  • Yousef, H. A., Hamdy, M., & Nashed, K. (2017). L1 adaptive fuzzy controller for a class of nonlinear systems with unknown backlash-like hysteresis. International Journal of Systems Science, 48(12), 2522–2533. https://doi.org/10.1080/00207721.2017.1324065
  • Yu, W., Bu, X., & Hou, Z. (2022). Security data-driven control for nonlinear systems subject to deception and false data injection attacks. IEEE Transactions on Network Science and Engineering, 9(4), 2910–2921. https://doi.org/10.1109/TNSE.2022.3173310.
  • Yu, Z., Yan, H., Li, S., & Dong, Y. (2017). Approximation-based adaptive tracking control for switched stochastic strict-feedback nonlinear time-delay systems with sector-bounded quantization input. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(12), 2145–2157. https://doi.org/10.1109/TSMC.2017.2721430
  • Zhou, J., Wen, C., & Zhang, Y. (2004). Adaptive backstepping control of a class of uncertain nonlinear systems with unknown backlash-like hysteresis. IEEE Transactions on Automatic Control, 49(10), 1751–1759. https://doi.org/10.1109/TAC.2004.835398
  • Zhou, Q., Li, H., Wang, L., & Lu, R. (2017). Prescribed performance observer-based adaptive fuzzy control for nonstrict-feedback stochastic nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(10), 1747–1758. https://doi.org/10.1109/TSMC.2017.2738155
  • Zhou, Q., Shi, P., Liu, H., & Xu, S. (2012). Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(6), 1608–1619. https://doi.org/10.1109/TSMCB.2012.2196432
  • Zhu, Q., Liu, Y., & Wen, G. (2020). Adaptive neural network output feedback control for stochastic nonlinear systems with full state constraints. ISA Transactions, 101, 60–68. https://doi.org/10.1016/j.isatra.2020.01.021

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.