CLC number: TP273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2024-03-03
Cited: 0
Clicked: 949
Yitao YANG, Lidong ZHANG. Event-triggered adaptive tracking control of a class of nonlinear systems with asymmetric time-varying output constraints[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(8): 1134-1144.
@article{title="Event-triggered adaptive tracking control of a class of nonlinear systems with asymmetric time-varying output constraints",
author="Yitao YANG, Lidong ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="8",
pages="1134-1144",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300679"
}
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%A Lidong ZHANG
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T1 - Event-triggered adaptive tracking control of a class of nonlinear systems with asymmetric time-varying output constraints
A1 - Yitao YANG
A1 - Lidong ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 8
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%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300679
Abstract: This article investigates the event-triggered adaptive neural network (NN) tracking control problem with deferred asymmetric time-varying (DATV) output constraints. To deal with the DATV output constraints, an asymmetric time-varying barrier Lyapunov function (ATBLF) is first built to make the stability analysis and the controller construction simpler. Second, an event-triggered adaptive NN tracking controller is constructed by incorporating an error-shifting function, which ensures that the tracking error converges to an arbitrarily small neighborhood of the origin within a predetermined settling time, consequently optimizing the utilization of network resources. It is theoretically proven that all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), while the initial value is outside the constraint boundary. Finally, a single-link robotic arm (SLRA) application example is employed to verify the viability of the acquired control algorithm.
[1]Adaldo A, Alderisio F, Liuzza D, et al., 2015. Event-triggered pinning control of switching networks. IEEE Trans Contr Netw Syst, 2(2):204-213.
[2]Cao L, Li HY, Dong GW, et al., 2021. Event-triggered control for multiagent systems with sensor faults and input saturation. IEEE Trans Syst Man Cybern Syst, 51(6):3855-3866.
[3]Du PH, Liang HJ, Zhao SY, et al., 2021. Neural-based decentralized adaptive finite-time control for nonlinear large-scale systems with time-varying output constraints. IEEE Trans Syst Man Cybern Syst, 51(5):3136-3147.
[4]Elmokadem T, Zribi M, Youcef-Toumi K, 2017. Terminal sliding mode control for the trajectory tracking of underactuated autonomous underwater vehicles. Ocean Eng, 129:613-625.
[5]Gao HJ, Li ZK, Yu XH, et al., 2022. Hierarchical multiobjective heuristic for PCB assembly optimization in a beam-head surface mounter. IEEE Trans Cybern, 52(7):6911-6924.
[6]Guerrero J, Torres J, Creuze V, et al., 2020. Adaptive disturbance observer for trajectory tracking control of underwater vehicles. Ocean Eng, 200:107080.
[7]He SD, Dai SL, Luo F, 2019. Asymptotic trajectory tracking control with guaranteed transient behavior for MSV with uncertain dynamics and external disturbances. IEEE Trans Ind Electron, 66(5):3712-3720.
[8]Heemels WPMH, Sandee JH, van den Bosch PPJ, 2008. Analysis of event-driven controllers for linear systems. Int J Contr, 81(4):571-590.
[9]Henningsson T, Johannesson E, Cervin A, 2008. Sporadic event-based control of first-order linear stochastic systems. Automatica, 44(11):2890-2895.
[10]Hu YB, Geng YH, Wu BL, et al., 2021. Model-free prescribed performance control for spacecraft attitude tracking. IEEE Trans Contr Syst Technol, 29(1):165-179.
[11]Li ML, Li TS, Gao XY, et al., 2020. Adaptive NN event-triggered control for path following of underactuated vessels with finite-time convergence. Neurocomputing, 379:203-213.
[12]Li XM, Zhou Q, Li PS, et al., 2020. Event-triggered consensus control for multi-agent systems against false data-injection attacks. IEEE Trans Cybern, 50(5):1856-1866.
[13]Liu Y, Chen XB, Wu YL, et al., 2022. Adaptive neural network control of a flexible spacecraft subject to input nonlinearity and asymmetric output constraint. IEEE Trans Neur Netw Learn Syst, 33(11):6226-6234.
[14]Liu YJ, Tong SC, 2016. Barrier Lyapunov functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints. Automatica, 64:70-75.
[15]Ma H, Li HY, Lu RQ, et al., 2020. Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances. Sci China Inform Sci, 63(5):150212.
[16]Peng ZH, Wang J, Han QL, 2019. Path-following control of autonomous underwater vehicles subject to velocity and input constraints via neurodynamic optimization. IEEE Trans Ind Electron, 66(11):8724-8732.
[17]Qiao L, Zhang WD, 2020. Trajectory tracking control of AUVs via adaptive fast nonsingular integral terminal sliding mode control. IEEE Trans Ind Inform, 16(2):1248-1258.
[18]Qiu JB, Wang T, Sun KK, et al., 2022. Disturbance observer-based adaptive fuzzy control for strict-feedback nonlinear systems with finite-time prescribed performance. IEEE Trans Fuzzy Syst, 30(4):1175-1184.
[19]Tabuada P, 2007. Event-triggered real-time scheduling of stabilizing control tasks. IEEE Trans Autom Contr, 52(9):1680-1685.
[20]Wang AQ, Liu L, Qiu JB, et al., 2019. Event-triggered robust adaptive fuzzy control for a class of nonlinear systems. IEEE Trans Fuzzy Syst, 27(8):1648-1658.
[21]Wang AQ, Liu L, Qiu JB, et al., 2020. Finite-time adaptive fuzzy control for nonstrict-feedback nonlinear systems via an event-triggered strategy. IEEE Trans Fuzzy Syst, 28(9):2164-2174.
[22]Wang XJ, Niu B, Song XM, et al., 2021. Neural networks-based adaptive practical preassigned finite-time fault tolerant control for nonlinear time-varying delay systems with full state constraints. Int J Robust Nonl Contr, 31(5):1497-1513.
[23]Xiang XB, Yu CY, Lapierre L, et al., 2018. Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles. Int J Fuzzy Syst, 20(2):572-586.
[24]Zhang CH, Yang GH, 2020. Event-triggered global finite-time control for a class of uncertain nonlinear systems. IEEE Trans Autom Contr, 65(3):1340-1347.
[25]Zhang JJ, Sun QM, 2020. Prescribed performance adaptive neural output feedback dynamic surface control for a class of strict-feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics. Int J Robust Nonl Contr, 30(2):459-483.
[26]Zhang JM, Niu B, Wang D, et al., 2022. Time-/event-triggered adaptive neural asymptotic tracking control for nonlinear systems with full-state constraints and application to a single-link robot. IEEE Trans Neur Netw Learn Syst, 33(11):6690-6700.
[27]Zhang S, Dong YT, Ouyang YC, et al., 2018. Adaptive neural control for robotic manipulators with output constraints and uncertainties. IEEE Trans Neur Netw Learn Syst, 29(11):5554-5564.
[28]Zhang TP, Xia MZ, Yi Y, 2017. Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics. Automatica, 81:232-239.
[29]Zhao K, Chen JW, 2020. Adaptive neural quantized control of MIMO nonlinear systems under actuation faults and time-varying output constraints. IEEE Trans Neur Netw Learn Syst, 31(9):3471-3481.
[30]Zhao K, Chen L, Chen CLP, 2022. Event-based adaptive neural control of nonlinear systems with deferred constraint. IEEE Trans Syst Man Cybern Syst, 52(10):6273-6282.
[31]Zhou SY, Song YD, Luo XS, 2020. Fault-tolerant tracking control with guaranteed performance for nonlinearly parameterized systems under uncertain initial conditions. J Franklin Inst, 357(11):6805-6823.
[32]Zong GD, Sun HB, Nguang SK, 2021. Decentralized adaptive neuro-output feedback saturated control for INS and its application to AUV. IEEE Trans Neur Netw Learn Syst, 32(12):5492-5501.
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