78
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

A model-free deep integral policy iteration structure for robust control of uncertain systems

, &
Pages 1571-1583 | Received 25 Sep 2023, Accepted 28 Jan 2024, Published online: 08 Feb 2024

References

  • Abu-Khalaf, M., & Lewis, F. L. (2005). Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach. Automatica, 41(5), 779–791. https://doi.org/10.1016/j.automatica.2004.11.034
  • Bhasin, S., Kamalapurkar, R., Johnson, M., Vamvoudakis, K. G., Lewis, F. L., & W. E. Dixon (2013). A novel actor–critic–identifier architecture for approximate optimal control of uncertain nonlinear systems. Automatica, 49(1), 82–92. https://doi.org/10.1016/j.automatica.2012.09.019
  • Duan, J., Li, J., Ge, Q., Li, S. E., Bujarbaruah, M., Ma, F., & Zhang, D. (2023). Relaxed actor-critic with convergence guarantees for continuous-time optimal control of nonlinear systems. IEEE Transactions on Intelligent Vehicles, 8(5), 3299–3311. https://doi.org/10.1109/TIV.2023.3255264
  • Guo, L., & Zhao, H. (2023). Online adaptive optimal control algorithm based on synchronous integral reinforcement learning with explorations. Neurocomputing, 520, 250–261. https://doi.org/10.1016/j.neucom.2022.11.055
  • Ha, M., Wang, D., & Liu, D. (2022). Discounted iterative adaptive critic designs with novel stability analysis for tracking control. IEEE/CAA Journal of Automatica Sinica, 9(7), 1262–1272. https://doi.org/10.1109/JAS.2022.105692
  • Jha, S. K., Roy, S. B., & Bhasin, S. (2019). Initial excitation-based iterative algorithm for approximate optimal control of completely unknown LTI systems. IEEE Transactions on Automatic Control, 64(12), 5230–5237. https://doi.org/10.1109/TAC.9
  • Jiang, H., Zhang, H., Luo, Y., & Han, J. (2019). Neural-network-based robust control schemes for nonlinear multiplayer systems with uncertainties via adaptive dynamic programming. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(3), 579–588. https://doi.org/10.1109/TSMC.6221021
  • Jiang, Y., & Jiang, Z. P. (2014). Robust adaptive dynamic programming and feedback stabilization of nonlinear systems. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 882–893. https://doi.org/10.1109/TNNLS.5962385
  • Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L. (2018). Optimal and autonomous control using reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2042–2062. https://doi.org/10.1109/TNNLS.2017.2773458
  • Lee, J. Y., Park, J. B., & Choi, Y. H. (2014). On integral generalized policy iteration for continuous-time linear quadratic regulations. Automatica, 50(2), 475–489. https://doi.org/10.1016/j.automatica.2013.12.009
  • Liu, D., Li, C., Li, H., Wang, D., & Ma, H. (2015). Neural-network-based decentralized control of continuous-time nonlinear interconnected systems with unknown dynamics. Neurocomputing, 165, 90–98. https://doi.org/10.1016/j.neucom.2014.07.082
  • Modares, H., & Lewis, F. L. (2014). Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning. Automatica, 50(7), 1780–1792. https://doi.org/10.1016/j.automatica.2014.05.011
  • Pang, B., Bian, T., & Jiang, Z. P. (2022). Robust policy iteration for continuous-time linear quadratic regulation. IEEE Transactions on Automatic Control, 67(1), 504–511. https://doi.org/10.1109/TAC.2021.3085510
  • Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2018). Multistep neural networks for data-driven discovery of nonlinear dynamical systems. arXiv preprint arXiv:1801.01236.
  • Vamvoudakis, K. G., & Lewis, F. L. (2010). Online actor critic algorithm to solve the continuous-time infinite horizon optimal control problem. Automatica, 46(5), 878–888. https://doi.org/10.1016/j.automatica.2010.02.018
  • Vrabie, D., & Lewis, F. (2009). Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems. Neural Networks, 22(3), 237–246. https://doi.org/10.1016/j.neunet.2009.03.008
  • Wallace, B. A., & Si, J. (in press). Continuous-time reinforcement learning control: A review of theoretical results, insights on performance, and needs for new designs. IEEE Transactions on Neural Networks and Learning Systems, 1–21. https://doi.org/10.1109/tnnls.2023.3245980
  • Wang, D., Cheng, L., & Yan, J. (2022). Self-learning robust control synthesis and trajectory tracking of uncertain dynamics. IEEE Transactions on Cybernetics, 52(1), 278–286. https://doi.org/10.1109/TCYB.2020.2979694
  • Wang, D., Gao, N., Liu, D., Li, J., & Lewis, F. (2024). Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications. IEEE/CAA Journal of Automatica Sinica, 11(1), 18–36. https://doi.org/10.1109/jas.2023.123843
  • Wang, D., He, H., & Liu, D. (2017). Adaptive critic nonlinear robust control: A survey. IEEE Transactions on Cybernetics, 47(10), 3429–3451. https://doi.org/10.1109/TCYB.2017.2712188
  • Wang, D., Hu, L., Zhao, M., & Qiao, J. (2023). Dual event-triggered constrained control through adaptive critic for discrete-time zero-sum games. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(3), 1584–1595. https://doi.org/10.1109/TSMC.2022.3201671
  • Wang, D., Li, C., Liu, D., & Mu, C. (2016). Data-based robust optimal control of continuous-time affine nonlinear systems with matched uncertainties. Information Sciences, 366, 121–133. https://doi.org/10.1016/j.ins.2016.05.034
  • Wang, D., Li, X., Zhao, M., & Qiao, J. (2024). Adaptive critic control design with knowledge transfer for wastewater treatment applications. IEEE Transactions on Industrial Informatics, 20(2), 1488–1497. https://doi.org/10.1109/tii.2023.3278875
  • Wang, D., & Mu, C. (2018). Adaptive-critic-based robust trajectory tracking of uncertain dynamics and its application to a spring–mass–damper system. IEEE Transactions on Industrial Electronics, 65(1), 654–663. https://doi.org/10.1109/TIE.41
  • Wang, D., Ren, J., Ha, M., & Qiao, J. (2023). System stability of learning-based linear optimal control with general discounted value iteration. IEEE Transactions on Neural Networks and Learning Systems, 24(9), 6504–6514. https://doi.org/10.1109/tnnls.2021.3137524
  • Wang, D., Wang, J., Zhao, M., Xin, P., & Qiao, J. (2023). Adaptive multi-step evaluation design with stability guarantee for discrete-time optimal learning control. IEEE/CAA Journal of Automatica Sinica, 10(9), 1797–1809. https://doi.org/10.1109/JAS.2023.123684
  • Wang, W., Chen, X., Fu, H., & Wu, M. (2019). Data-driven adaptive dynamic programming for partially observable nonzero-sum games via Q-learning method. International Journal of Systems Science, 50(7), 1338–1352. https://doi.org/10.1080/00207721.2019.1599463
  • Wei, Q., Li, H., Yang, X., & He, H. (2021). Continuous-time distributed policy iteration for multicontroller nonlinear systems. IEEE Transactions on Cybernetics, 51(5), 2372–2383. https://doi.org/10.1109/TCYB.2020.2979614
  • Wu, Q., Zhao, B., & Liu, D. (2020). Adaptive dynamic programming-based decentralised control for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay. International Journal of Systems Science, 51(15), 2883–2898. https://doi.org/10.1080/00207721.2020.1803439
  • Xiao, G., Zhang, H., Zhang, K., & Wen, Y. (2018). Value iteration based integral reinforcement learning approach for H∞ controller design of continuous-time nonlinear systems. Neurocomputing, 285, 51–59. https://doi.org/10.1016/j.neucom.2018.01.029
  • Yang, J., Chen, W., & Li, S. (2023). Non-linear disturbance observer-based robust control for systems with mismatched disturbances/uncertainties. IET Control Theory and Applications, 5(18), 2053–2062. https://doi.org/10.1049/iet-cta.2010.0616
  • Yang, Y., Fan, X., Sun, B., Xu, C., Zuo, S., & Yue, D. (2022). Event-triggered adaptive approximately optimal tracking control of a class of non-affine SISO nonlinear systems via output feedback. International Journal of Systems Science, 53(2), 223–239. https://doi.org/10.1080/00207721.2021.1947410
  • Yang, Y., Gao, W., Modares, H., & Xu, C. Z. (2022). Robust actor–critic learning for continuous-time nonlinear systems with unmodeled dynamics. IEEE Transactions on Fuzzy Systems, 30(6), 2101–2112. https://doi.org/10.1109/TFUZZ.2021.3075501
  • Zhao, M., Wang, D., Qiao, J., Ha, M., & Ren, J. (2023). Advanced value iteration for discrete-time intelligent critic control: A survey. Artificial Intelligence Review, 56, 12315–12346. https://doi.org/10.1007/s10462-023-10497-1

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.