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Observer-based adaptive dynamic surface control of time-delayed non-strict systems with input nonlinearities. (English) Zbl 1516.93138

Summary: This paper proposes an adaptive dynamic surface controller for uncertain time-delay non-strict nonlinear systems with unknown control direction and unknown dead zone. To this end, the problem of uncertainty in nonlinear terms of the overall system is managed such that the estimation of these terms is obtained by applying a fuzzy logic, which is established based on an adaptive approach. A particular observer is then designed to approximate the immeasurable states. Furthermore, to overcome the delay issue in the system, the Lyapunov Krasovskii functional is used to achieve design conditions for dynamic surface control. Moreover, the breach of the output in the system is addressed by employing a Barrier Lyapunov Function. Then, with the aim of the designed controller, the stability of the closed-loop system is ensured such that all states are limited, and the errors are semi-globally uniformly ultimately bounded (SGUUB). Finally, as an illustration of the effectiveness of the proposed controller, a practical simulation is provided.

MSC:

93C40 Adaptive control/observation systems
93B53 Observers
93C41 Control/observation systems with incomplete information
93C43 Delay control/observation systems
93C10 Nonlinear systems in control theory
Full Text: DOI

References:

[1] Khalil, H. K.; Grizzle, J., Nonlinear Systems (1996), Prentice Hall New Jersey
[2] Krstic, M.; Kanellakopoulos, I.; Kokotovic, P. V., Nonlinear and Adaptive Control Design (1995), Wiley · Zbl 0763.93043
[3] Li, G.; Yu, J.; Chen, X., Adaptive fuzzy neural network command filtered impedance control of constrained robotic manipulators with disturbance observer, IEEE Trans. Neural Netw. Learn. Syst. (2021)
[4] Fu, C.; Wang, Q. G.; Yu, J.; Lin, C., Neural network-based finite-time command filtering control for switched nonlinear systems with backlash-like hysteresis, IEEE Trans. Neural Netw. Learn. Syst., 32.7, 3268-3273 (2020)
[5] Yip, P. P.; Hedrick, J. K., Adaptive dynamic surface control: a simplified algorithm for adaptive backstepping control of nonlinear systems, Int. J. Control, 71, 5, 959-979 (1998) · Zbl 0969.93037
[6] Yu, J.; Cheng, S.; Shi, P.; Lin, C., Command-filtered neuroadaptive output-feedback control for stochastic nonlinear systems with input constraint, IEEE Trans. Cybern. (2021)
[7] Tong, M.; Lin, W.; Huo, X., A model-free fuzzy adaptive trajectory tracking control algorithm based on dynamic surface control, Int. J. Adv. Robot. Syst., 17 (2020)
[8] Ma, H.; Liang, H.; Zhou, Q.; Ahn, C. K., Adaptive dynamic surface control design for uncertain nonlinear strict-feedback systems with unknown control direction and disturbances, IEEE Trans. Syst. Man Cybern. Syst., 49, 3, 506-515 (2018)
[9] Han, H.; Chen, J.; Karimi, H. R., State and disturbance observers-based polynomial fuzzy controller, Inf. Sci., 382-383, 38-59 (2017) · Zbl 1432.93196
[10] Li, H.; Chen, Z.; Sun, Y., Stabilization for a class of nonlinear networked control systems via polynomial fuzzy model approach, Complex., 21, 2, 74-81 (2015)
[11] Shojaei, F.; Arefi, M. M.; Khayatian, A., Observer-based adaptive-fuzzy dsc of strict-feedback nonlinear delayed systems with unknown control direction, Tabriz J. Electric. Eng., 48, 1, 71-88 (2018)
[12] Qin, H.; Li, X.; Sun, Y., Distributed adaptive coordinated control of multiple Euler-Lagrange systems considering output constraints and time delays, Complex. (2021)
[13] Zeng, W.; Li, Z.; Gao, C.; Wu, L., Observer-based adaptive fuzzy control for strict-feedback nonlinear systems with prescribed performance and dead zone, International J. Control Autom. Syst., 19, 5, 1962-1975 (2021)
[14] Sun, W.; Wang, L.; Wu, Y., Adaptive dynamic surface fuzzy control for state constrained time-delay nonlinear nonstrict feedback systems with unknown control directions, IEEE Trans. Syst. Man Cybern. Syst. (2020)
[15] Zhang, X., Fuzzy approximator based adaptive dynamic surface control for unknown time-delay nonlinear systems with input asymmetric hysteresis nonlinearities, IEEE Trans. Syst. Man Cybern. Syst., 47, 8, 2218-2232 (2017)
[16] Chen, Z., Nussbaum functions in adaptive control with time-varying unknown control coefficients, Automatica, 102, 72-79 (2019) · Zbl 1415.93140
[17] Shi, W.; Xu, L.; Chen, S., Dynamic surface control using fuzzy logic systems for uncertain, strict-feedback, nonlinear systems with unknown control directions, IEEJ Trans. Electr. Electron. Eng., 15, 1, 140-150 (2020)
[18] Habibi, H.; Nohooji, H. R.; Howard, I., Backstepping Nussbaum gain dynamic surface control for a class of input and state constrained systems with actuator faults, Inf. Sci., 482, 27-46 (2019) · Zbl 1453.93128
[19] Boulkroune, A.; M’Saad, M.; Chekireb, H., Design of a fuzzy adaptive controller for MIMO nonlinear time-delay systems with unknown actuator nonlinearities and unknown control direction, Inf. Sci., 180, 24, 5041-5059 (2010) · Zbl 1205.93086
[20] Nussbaum, R. D., Some remarks on a conjecture in parameter adaptive control, Syst. Control Lett., 3, 5, 243-246 (1983) · Zbl 0524.93037
[21] Chen, P.; Zhang, T., Adaptive dynamic surface control of stochastic nonstrict-feedback constrained nonlinear systems with input and state unmodeled dynamics, Int. J. Adapt. Control Signal Process., 34, 10, 1405-1429 (2020) · Zbl 1522.93088
[22] Zhang, T.; Lin, M.; Xia, X.; Yi, Y., Adaptive cooperative dynamic surface control of non-strict feedback multi-agent systems with input dead-zones and actuator failures, Neurocomputing, 442, 48-63 (2021)
[23] Wu, J., Adaptive neural dynamic surface control with prespecified tracking accuracy of uncertain stochastic nonstrict-feedback systems, IEEE Trans. Cybern. (2020)
[24] Wan, M.; Yin, Y., Adaptive dynamic surface control based on observer for switched non-strict feedback systems with full state constraints, IEEE Access, 8, 71008-71020 (2020)
[25] Wu, Z.; Zhang, T., Finit-Time adaptive neural dynamic surface control of nonstrict feedback nonlinear systems including dead-zone and full state restrictions, IEEE Access, 8, 186699-186709 (2020)
[26] Park, J. H.; Shen, H.; Chang, X. H.; Lee, T. H., Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals (2019), Springer International Publishing · Zbl 1429.93004
[27] 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) · Zbl 1162.93346
[28] Gao, T.; Li, T.; Liu, Y. J.; Tong, S., IBLF-based adaptive neural control of state-constrained uncertain stochastic nonlinear systems, IEEE Trans. Neural Netw. Learn. Syst. (2021)
[29] Gao, T.; Liu, Y. J.; Li, D.; Tong, S.; Li, T., Adaptive neural control using tangent time-varying BLFs for a class of uncertain stochastic nonlinear systems with full state constraints, IEEE Trans. Cybern., 51, 4, 1943-1953 (2019)
[30] Bahreini, M.; Zarei, J.; Razavi-Far, R.; Saif, M., Robust and reliable output feedback control for uncertain networked control systems against actuator faults, IEEE Trans. Syst. Man Cybern. Syst., 52, 4, 2555-2564 (2021)
[31] Bahreini, M.; Zarei, J., Robust fault-tolerant control for networked control systems subject to random delays via static-output feedback, ISA Trans., 86, 153-162 (2019)
[32] A. Torabi; Zarei, J.; Razavi-Far, R.; Saif, M., Decentralized resilient output-feedback control design for networked control systems under denial-of-service, IEEE Syst. J. (2021)
[33] Arefi, M. M.; Zarei, J.; Karimi, H. R., Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction, J. Franklin Inst., 351, 8, 4302-4316 (2014) · Zbl 1294.93050
[34] Wang, H.; Liu, K.; Liu, X.; Chen, B.; Lin, C., Neural-based adaptive output-feedback control for a class of nonstrict-feedback stochastic nonlinear systems, IEEE Trans. Cybern., 45, 9, 1977-1987 (2014)
[35] Yuan, J.; Ding, S.; Mei, K., Fixed-time SOSM controller design with output constraint, Nonlinear Dyn., 102, 3, 1567-1583 (2020) · Zbl 1517.93017
[36] Fang, L.; Ding, S.; Park, J. H.; Ma, L., Adaptive fuzzy output-feedback control design for a class of p-norm stochastic nonlinear systems with output constraints, IEEE Trans. Circuits Syst. Regul. Pap., 68, 6, 2626-2638 (2021)
[37] Wan, M.; Huang, S., Adaptive dynamic surface decentralized output feedback control for switched nonstrict feedback large-scale systems with unknown dead zones, Math. Probl. Eng. (2020) · Zbl 1459.93086
[38] Li, Z.; Li, T.; Feng, G.; Zhao, R.; Shan, Q., Neural network-based adaptive control for pure-feedback stochastic nonlinear systems with time-varying delays and dead-zone input, IEEE Trans. Syst. Man Cybern. Syst., 50, 12, 5317-5329 (2018)
[39] Nie, S., Barrier Lyapunov function-based dynamic surface control with tracking error constraints for ammunition manipulator electro-hydraulic system, Defence Technol., 17, 3, 836-845 (2021)
[40] Shojaei, F.; Arefi, M. M.; Khayatian, A.; Karimi, H. R., Observer-based fuzzy adaptive dynamic surface control of uncertain nonstrict feedback systems with unknown control direction and unknown dead-zone, IEEE Trans. Syst. Man Cybern. Syst. (2018)
[41] Yang, H.; Shi, P.; Zhao, X.; Shi, Y., Adaptive output-feedback neural tracking control for a class of nonstrict-feedback nonlinear systems, Inf. Sci., 205-218 (2016) · Zbl 1396.93076
[42] Zhou, Q.; Shi, P.; Xu, S.; Li, H., Adaptive output feedback control for nonlinear time-delay systems by fuzzy approximation approach, IEEE Trans. Fuzzy Syst., 21, 2, 301-313 (2012)
[43] Gao, Y.; Tong, S.; Li, Y., Observer-based adaptive fuzzy output constrained control for MIMO nonlinear systems with unknown control directions, Fuzzy Sets Syst., 290, 79-99 (2016) · Zbl 1374.93211
[44] Tong, S.; Li, Y.; Sui, S., Adaptive fuzzy output feedback control for switched nonstrict-feedback nonlinear systems with input nonlinearities, IEEE Trans. Fuzzy Syst., 24, 6, 1426-1440 (2016)
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