Article
Full entry |
PDF
(5.7 MB)
Feedback
PDF
(5.7 MB)
Feedback
Keywords:
optimal control; unknown nonlinear system; adaptive dynamic programming; identifier-critic neural networks; event-triggered mechanism
optimal control; unknown nonlinear system; adaptive dynamic programming; identifier-critic neural networks; event-triggered mechanism
Summary:
This paper proposes an online identifier-critic learning framework for event-triggered optimal control of completely unknown nonlinear systems. Unlike classical adaptive dynamic programming (ADP) methods with actor-critic neural networks (NNs), a filter-regression-based approach is developed to reconstruct the unknown system dynamics, and thus avoid the dependence on an accurate system model in the control design loop. Meanwhile, NN adaptive laws are designed for the parameter estimation by using only the measured system state and input data, and facilitate the identifier-critic NN design. The convergence of the adaptive laws is analyzed. Furthermore, in order to reduce state sampling frequency, two kinds of aperiodic sampling schemes, namely static and dynamic event triggers, are embedded into the proposed optimal control design. Finally, simulation results are presented to demonstrate the effectiveness of the proposed event-triggered optimal control strategy.
References:
[1] Bhasin, S., Kamalapurkar, R., Johnson, M., Vamvoudakis, K. G., Lewis, F. L., Dixon, W. E.: A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems. Automatica 49 (2013), 82-92. DOI | MR 2999950
[2] Chen, B., Hu, J., Zhao, Y., Ghosh, B. K.: Finite-time observer based tracking control of uncertain heterogeneous underwater vehicles using adaptive sliding mode approach. Neurocomputing 481 (2022), 322-332. DOI
[3] Fu, X., Li, Z.: Neural network optimal control for nonlinear system based on zero-sum differential game. Kybernetika 57 (2021), 546-566. DOI | MR 4299463
[4] Girard, A.: Dynamic triggering mechanisms for event-triggered control. IEEE Trans. Autom. Control 60 (2015), 1992-1997. DOI | MR 3365092
[5] Hu, J., Chen, G., Li, H.-X.: Distributed event-triggered tracking control of leader-follower multi-agent systems with communication delays. Kybernetika 47 (2011), 630-643. MR 2884865 | Zbl 1227.93008
[6] Hu, J., Geng, J., Zhu, H.: An observer-based consensus tracking control and application to event-triggered tracking. Commun. Nonlinear Sci. Numer. Simul. 20 (2015), 559-570. DOI | MR 3251515 | Zbl 1303.93012
[7] Jiang, Y., Jiang, Z. P.: Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics. Automatica 48 (2012), 2699-2704. DOI | MR 2961173
[8] Khalil, H. K.: Nonlinear Systems. Third Edition. Prentice-Hallm Upper Saddle River, NJ 2002. Zbl 1194.93083
[9] Kiumarsi, B., Lewis, F. L.: Actor-critic-based optimal tracking for partially unknown nonlinear discrete-time systems. IEEE Trans. Neural Netw. Learn. Syst. 26 (2015), 140-151. DOI | MR 3449569
[10] Kreisselmeier, G.: Adaptive observers with exponential rate of convergence. IEEE Trans. Autom. Control AC-22 (1977), 2-8. DOI 10.1109/TAC.1977.1101401 | MR 0444142
[11] Lewis, F., Jagannathan, S., Yesildirak, A.: Neural Network Control of Robot Manipulators and Nonlinear Systems. Taylor and Francis, London 1999.
[12] Lewis, F. L., Vrabie, D. L., Syrmos, V. L.: Optimal Control. Third Edition. Wiley, New York 2012. DOI | MR 2953185
[13] Luo, R., Peng, Z., Hu, J., Bijoy, B. K.: Adaptive optimal control of completely unknown systems with relaxed PE conditions. In: Proc. IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS), Chengdu 2022, pp. 836-841. DOI
[14] Lv, Y., Na, J., Yang, Q., Wu, X., Guo, Y.: Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics. Int. J. Control 89 (2016), 99-112. DOI | MR 3433390
[15] Luo, R., Peng, Z., Hu, J.: On model identification based optimal control and it's applications to multi-agent learning and control. Mathematics 11 (2023), 906. DOI
[16] Makumi, W., Greene, M. L., Bell, Z., Bialy, B., Kamalapurkar, R., Dixon, W.: Hierarchical reinforcement learning and gains cheduling-based control of a hypersonic vehicle. AIAA SCITECH 2023 Forum,National Harbor, MD and Online, 2023, 1-11. DOI
[17] Ouyang, Y., Dong, L., Sun, C.: Critic learning-based control for robotic manipulators with prescribed constraints. IEEE Trans. Cybern. 52 (2022), 2274-2283. DOI
[18] Peng, Z., Luo, R., Hu, J., Shi, K., Ghosh, B. K.: Distributed optimal tracking control of discrete-time multiagent systems via event-triggered reinforcement learning. IEEE Trans. Circuits Syst. I-Regul. Pap. 69 (2022), 3689-3700. DOI
[19] Peng, Z., Luo, R., Hu, J., Shi, K., Nguang, S. K., Ghosh, B. K.: Optimal tracking control of nonlinear multiagent systems using internal reinforce Q-learning. IEEE Trans. Neural Netw. Learn. Syst. 33 (2022), 4043-4055. DOI | MR 4468295
[20] Peng, Z., Zhao, Y., Hu, J., Luo, R., Ghosh, B. K., Nguang, S. K.: Input-output data-based output antisynchronization control of multiagent systems using reinforcement learning approach. IEEE Trans. Ind. Inform. 17 (2021), 7359-7367. DOI
[21] 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. to be published. DOI
[22] Song, R., Lewis, F., Wei, Q., Zhang, H. G., Jiang, Z. P., Levine, D.: Multiple actor-critic structures for continuous-time optimal control using input-output data. IEEE Trans. Neural Netw. Learn. Syst. 26 (2015), 851-865. DOI | MR 3452493
[23] Tabuada, P.: Event-triggered real-time scheduling of stabilizing control tasks. IEEE Trans. Autom. Control 52 (2007), 1680-1685. DOI | MR 2352444
[24] Wang, K., Mu, C.: Event-sampled learning for unknown nonlinear systems related to dynamic triggering method. In: Proc. IEEE Conference on Decision and Control (CDC), Jeju 2020, pp. 5200-5205. DOI
[25] Wang, D., Mu, C., Liu, D.: Adaptive critic designs for solving event-based $H_\infty$ control problems. In: Proc. American Control Conference (ACC), Seattle 2017, pp. 2435-2400. DOI
[26] Wang, X., Qin, W., Park, J. H., Shen, M.: Event-triggered data-driven control of discrete-time nonlinear systems with unknown disturbance. ISA Trans. 128 (2022), 256-264. DOI
[27] Werbos, P. J.: Approximate dynamic programming for real-time control and neural modeling. In: Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches (D. A. White and D. A. Sofge, Eds.), Van Nostrand Reinhold, New York 1992, ch. 13.
[28] Xu, N., Niu, B., Wang, H., Huo, X., Zhao, X.: Single-network ADP for solving optimal event-triggered tracking control problem of completely unknown nonlinear systems. Int. J. Intell. Syst. 36 (2021), 4795-4815. DOI
[29] Xue, S., Luo, B., Liu, D., Gao, Y.: Adaptive dynamic programming-based event-triggered optimal tracking control. Int. J. Robust Nonlinear Control 31 (2021), 7480-7497. DOI | MR 4335306
[30] Yang, X., He, H.: Adaptive critic designs for event-triggered robust control of nonlinear systems with unknown dynamics. IEEE Trans. Cybern. 49 (2019), 2255-2267. DOI
[31] Yang, X., He, H., Liu, D.: Event-triggered optimal neuro-controller design with reinforcement learning for unknown nonlinear systems. IEEE Trans. Syst. Man Cybern. Syst. 49 (2019), 1866-1878. DOI
Search
Partner of
