Similar articles

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.

具有非对称时变输出约束的一类非线性系统的事件触发自适应跟踪控制

[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.