Abstract
A novel dynamic event-triggered control strategy is proposed by utilizing the adaptive critic learning (ACL) technique for nonlinear continuous-time systems subject to disturbances in this paper. To address the transformation of the robust-optimal control problem, a modified cost function containing the disturbance term is introduced. The dynamic event-triggered controller is obtained by incorporating an internal variable into the static triggering strategy. An ACL method is employed to design static and dynamic triggering controllers. Critic neural networks are used to approximate the cost function and the corresponding Hamilton–Jacobi–Bellman equation, leading to the establishment of adaptive critic event-triggered controllers. Stability analysis of the closed-loop system is also provided, and simulation results demonstrate the effectiveness of the developed dynamic triggering strategy with two examples.
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References
Wang, D., Ha, M., Zhao, M.: The intelligent critic framework for advanced optimal control. Artif Intell Rev 55(1), 1–22 (2022)
Chen, Z., Hu, J., Min, G., Luo, C., El-Ghazawi, T.: Adaptive and efficient resource allocation in cloud datacenters using cctor-critic deep reinforcement learning. IEEE Trans. Parallel Distrib. Syst. 33(8), 1911–1923 (2022)
Ha, M., Wang, D., Liu, D.: Discounted iterative adaptive critic designs with novel stability analysis for tracking control. IEEE/CAA J Automat Sin 9(7), 1262–1272 (2022)
Yang, X., Zeng, Z., Gao, Z.: Decentralized neurocontroller design with critic learning for nonlinear-interconnected systems. IEEE Trans Cybern 52(11), 11672–11685 (2022)
Wang, D., Hu, L., Zhao, M., Qiao, J.: Dual event-triggered constrained control through adaptive critic for discrete-time zero-sum games. IEEE Trans Syst Man Cybern Syst 53(3), 1584–1595 (2023)
Zamfirache, I.A., Precup, R.-E., Roman, R.-C., Petriu, E.M.: Policy iteration reinforcement learning-based control using a grey wolf optimizer algorithm. Inf. Sci. 585, 162–175 (2022)
Shen, M., Wang, X., Park, J.H., Yi, Y., Che, W.-W.: Extended disturbance-observer-based data-driven control of networked nonlinear systems with event-triggered output. IEEE Trans Syst Man Cybern Syst 53(5), 3129–3140 (2023)
Wang, D., Zhao, M., Ha, M., Qiao, J.: Intelligent optimal tracking with application verifications via discounted generalized value iteration. Acta Autom Sin 48(1), 182–193 (2022)
Wang, D., Cheng, L., Yan, J.: Self-learning robust control synthesis and trajectory tracking of uncertain dynamics. IEEE Trans Cybern 55(1), 278–286 (2022)
Wang, D., Ren, J., Ha, M., Qiao, J.: System stability of learning-based linear optimal control with general discounted value iteration. IEEE Trans Neural Netw Learn Syst (2021). https://doi.org/10.1109/TNNLS.2021.3137524
Huo, Y., Wang, D., Qiao, J., Li, M.: Adaptive critic design for nonlinear multi-player zero-sum games with unknown dynamics and control constraints. Nonlinear Dyn (2023). https://doi.org/10.1007/s11071-023-08419-5
Zhao, Q., Sun, J., Wang, G., Chen, J.: Event-triggered ADP for nonzero-sum games of unknown nonlinear systems. IEEE Trans Neural Netw Learn Syst 33(5), 1905–1913 (2022)
Zhao, S., Wang, J.: Robust optimal control for constrained uncertain switched systems subjected to input saturation: the adaptive event-triggered case. Nonlinear Dyn. 110, 363–380 (2022). https://doi.org/10.1007/s11071-022-07624-y
Zhang, Y., Zhao, B., Liu, D., Zhang, S.: Event-triggered control of discrete-time zero-sum games via deterministic policy gradient adaptive dynamic programming. IEEE Trans Syst Man Cybern Syst 52(8), 4823–4835 (2022)
Zhang, H., Zhang, K., Xiao, G., Jiang, H.: Robust optimal control scheme for unknown constrained-input nonlinear systems via a plug-n-play event-sampled critic-only algorithm. IEEE Trans Syst Man Cybern Syst 50(9), 3169–3180 (2020)
Jiang, Y., Jiang, Z.-P.: Robust adaptive dynamic programming and feedback stabilization of nonlinear systems. IEEE Trans Neural Netw Learn Syst 25(5), 882–893 (2014)
Yan, S., Gu, Z., Park, J.H., Xie, X.: Sampled memory-event-triggered fuzzy load frequency control for wind power systems subject to outliers and transmission delays. IEEE Trans Cybern 53(6), 4043–4053 (2023)
Lin, F.: Robust Control Design: An Optimal Control Approach. Wiley, USA (2007)
Wang, D., Liu, D., Li, H.: Policy iteration algorithm for online design of robust control for a class of continuous-time nonlinear systems. IEEE Trans. Autom. Sci. Eng. 11(2), 627–632 (2014)
Werbos, P.J.: Handbook of Intelligent Control: Neural, Fuzzy and Adaptive Approaches-Approximate Dynamic Programming for Realtime Control and Neural Modeling. Van Nostrand, New York, NY, USA (1992)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Pretice-Hall, Upper Saddle River, NJ, USA (1999)
Poznyak, A.S., Yu, W., Sanchez, E.N.: Identification and control of unknown chaotic systems via dynamic neural networks. IEEE Trans Circuits Syst I Fundam Theory Appl 46(12), 1491–1495 (1999)
Vrabie, D., Lewis, F.L.: Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems. Neural Netw. 22(3), 237–246 (2009)
Liu, D., Xue, S., Zhao, B., Luo, B., Wei, Q.: Adaptive dynamic programming for control: A survey and recent advances. IEEE Trans Syst Man Cybern Syst 51(1), 142–160 (2021)
Li, Z., Yue, D., Ma, Y., Zhao, J.: Neural-networks-based prescribed tracking for nonaffine switched nonlinear time-delay systems. IEEE Trans Cybern 52(7), 6579–6590 (2022)
Vamvoudakis, K.G., Lewis, F.L.: Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem. Automatica 46(5), 878–888 (2010)
Xue, S., Luo, B., Liu, D.: Event-triggered adaptive dynamic programming for unmatched uncertain nonlinear continuous-time systems. IEEE Trans Neural Netw Learn Syst 32(7), 2939–2951 (2021)
Gu, Y., Park, J.H., Shen, M., Liu, D.: Event-triggered control of Markov jump systems against general transition probabilities and multiple disturbances via adaptive-disturbance-observer approach. Inf. Sci. 608, 1113–1130 (2022)
Zhang, H., Zhang, K., Xiao, G., Jiang, H.: Robust optimal control scheme for unknown constrained-input nonlinear systems via a plug-n-play event-sampled critic-only algorithm. IEEE Trans Syst Man Cybern Syst 50(9), 3169–3180 (2020)
Wang, S., Jin, X., Mao, S., Vasilakos, A.V., Tang, Y.: Model-free event-triggered optimal consensus control of multiple euler-lagrange systems via reinforcement learning. IEEE Trans Netw Sci Eng 8(1), 246–258 (2021)
Ren, H., Zong, G., Li, T.: Event-triggered finite-time control for networked switched linear systems with asynchronous switching. IEEE Trans Syst Man Cybern Syst 48(11), 1874–1884 (2018)
Xue, S., Luo, B., Liu, D., Gao, Y.: Event-triggered ADP for tracking control of partially unknown constrained uncertain systems. IEEE Trans Cybern 52(9), 9001–9012 (2022)
Luo, B., Yang, Y., Liu, D., Wu, H.-N.: Event-triggered optimal control with performance guarantees using adaptive dynamic programming. IEEE Trans Neural Netw Learn Syst 31(1), 76–88 (2020)
Yang, X., Wei, Q.: Adaptive critic learning for constrained optimal event-triggered control with discounted cost. IEEE Trans Neural Netw Learn Syst 32(1), 91–104 (2021)
Girard, A.: Dynamic triggering mechanisms for event-triggered control. IEEE Trans. Autom. Control 60(7), 1992–1997 (2015)
Liu, K.-Z., Teel, A.R., Sun, X.-M., Wang, X.-F.: Model-based dynamic event-triggered control for systems with uncertainty: A hybrid system approach. IEEE Trans. Autom. Control 66(1), 444–451 (2021)
Mu, C., Wang, K., Ni, Z.: Adaptive learning and sampled-control for nonlinear game systems using dynamic event-triggering strategy. IEEE Trans Neural Netw Learn Syst 33(9), 4437–4450 (2022)
Vamvoudakis, K.G.: Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems. IEEE/CAA J Autom Sin 1(3), 282–293 (2014)
Zhu, Y., Zhao, D., He, H., Ji, J.: Event-triggered optimal control for partially unknown constrained-input systems via adaptive dynamic programming. IEEE Trans. Industr. Electron. 64(5), 4101–4109 (2017)
Modares, H., Lewis, F.L., Naghibi-Sistani, M.-B.: Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems. Automatica 50(1), 193–202 (2014)
Vamvoudakis, K.G., Miranda, M.F., Hespanha, J.P.: Asymptotically stable adaptive-optimal control algorithm with saturating actuators and relaxed persistence of excitation. IEEE Trans Neural Netw Learn Syst 27(11), 2386–2398 (2016)
Wang, D., Liu, D.: Learning and guaranteed cost control with event-based adaptive critic implementation. IEEE Trans Neural Netw Learn Syst 29(12), 6004–6014 (2018)
Wang, D., Mu, C., Liu, D., Ma, H.: On mixed data and event driven design for adaptive-criticbased nonlinear \(H_{\infty }\) control. IEEE Trans Neural Netw Learn Syst 29(4), 993–1005 (2018)
Khalil, H.K.: Nonlinear Systems. Prentice-Hall, Upper Saddle River, NJ, USA (1996)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 62222301, Grant 61890930-5, and Grant 62021003; in part by the National Key Research and Development Program of China under Grant 2021ZD0112302, Grant 2021ZD0112301, and Grant 2018YFC1900800-5; and in part by the Beijing Natural Science Foundation under Grant JQ19013.
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Wang, D., Zhou, Z., Liu, A. et al. Event-triggered robust adaptive critic control for nonlinear disturbed systems. Nonlinear Dyn 111, 19963–19977 (2023). https://doi.org/10.1007/s11071-023-08862-4
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DOI: https://doi.org/10.1007/s11071-023-08862-4