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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
C. Gu, X. Wang, K. Li, X. Yin, S. Li, and L. Wang, “Enhanced tube-based event-triggered stochastic model predictive control with additive uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 0, pp. 1–10, Oct. 2024.
Citation: C. Gu, X. Wang, K. Li, X. Yin, S. Li, and L. Wang, “Enhanced tube-based event-triggered stochastic model predictive control with additive uncertainties,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 0, pp. 1–10, Oct. 2024.
shu

Enhanced Tube-Based Event-Triggered Stochastic Model Predictive Control With Additive Uncertainties

Funds:  This work was supported by the National Nature Science Foundation of China (62073194), the Natural Science Foundation of Shandong Province of China (ZR2023MF028), and the Taishan Scholars Program of Shandong Province (tsqn202312008)
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    Abstract
  • This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant (LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system uncertainties. Assisted with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of an HVAC system confirm the efficacy of the proposed control.

     

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  • [1]
    T. Li, X. Sun, G. Lei, Y. Guo, Z. Yang, and J. Zhu, “Finite-control-set model predictive control of permanent magnet synchronous motor drive systems—An overview,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2087–2105, Sept. 2022. doi: 10.1109/JAS.2022.105851
    [2]
    L. D'Alfonso, F. Giannini, G. Franzè, G. Fedele, F. Pupo, and G. Fortino, “Autonomous vehicle platoons in urban road networks: A joint distributed reinforcement learning and model predictive control approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 141–156, Jan. 2024. doi: 10.1109/JAS.2023.123705
    [3]
    T. Bai, S. Li, and Y. Zheng, “Distributed model predictive control for networked plant-wide systems with neighborhood cooperation,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 108–117, Jan. 2019. doi: 10.1109/JAS.2019.1911333
    [4]
    H. Wei and Y. Shi, “Mpc-based motion planning and control enables smarter and safer autonomous marine vehicles: perspectives and a tutorial survey,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 8–24, Jan. 2022.
    [5]
    M. Farina, L. Giulioni, and R. Scattolini, “Stochastic linear model predictive control with chance constraints—A review,” J. Process Control, vol. 44, pp. 56–67, Aug. 2016.
    [6]
    X. Ping, J. Hu, T. Lin, B. Ding, P. Wang, and Z. Li, “A survey of output feedback robust MPC for linear parameter varying systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1717–1751, Oct. 2022. doi: 10.1109/JAS.2022.105605
    [7]
    F. Li, H. Li, and Y. He, “Stochastic model predictive control for linear systems with unbounded additive uncertainties,” J. Frankl. Inst.-Eng. Appl. Math., vol. 359, no. 7, pp. 3024–3045, May 2022. doi: 10.1016/j.jfranklin.2022.02.004
    [8]
    A. Mesbah, “Stochastic model predictive control: An overview and perspectives for future research,” IEEE Control Syst. Mag., vol. 36, no. 6, pp. 30–44, Dec. 2016. doi: 10.1109/MCS.2016.2602087
    [9]
    M. C. Campi and S. Garatti, “A sampling-and-discarding approach to chance-constrained optimization: feasibility and optimality,” J. Optim. Theory Appl., vol. 148, no. 2, pp. 257–280, Feb. 2011. doi: 10.1007/s10957-010-9754-6
    [10]
    M. Lorenzen, F. Dabbene, R, Tempo, and F. Allgöwer, “Constraint-tightening and dtability in dtochastic model predictive control,” IEEE Trans. Autom. Control, vol. 62, no. 7, pp. 3165–3177, Jul. 2017. doi: 10.1109/TAC.2016.2625048
    [11]
    C. Shang and F. You, “A data-driven robust optimization approach to scenario-based stochastic model predictive control,” J. Process Control, vol. 75, pp. 24–39, Mar. 2019. doi: 10.1016/j.jprocont.2018.12.013
    [12]
    J. A. Paulson, E. A. Buehler, R. D. Braatz, and A. Mesbah, “Stochastic model predictive control with joint chance constraints,” Int. J. Control, vol. 93, no. 1, pp. 126–139, Oct. 2020. doi: 10.1080/00207179.2017.1323351
    [13]
    S. Yan, P. Goulart, and M. Cannon, “Stochastic MPC with dynamic feedback gain selection and discounted probabilistic constraints,” IEEE Trans. Autom. Control, vol. 67, no. 11, pp. 5885–5899, Nov. 2022. doi: 10.1109/TAC.2021.3128466
    [14]
    L. Hewing and M. N. Zeilinger, “Scenario-based probabilistic reachable sets for recursively feasible stochastic model predictive control,” IEEE Control Syst. Lett., vol. 4, no. 2, pp. 450–455, Apr. 2020. doi: 10.1109/LCSYS.2019.2949194
    [15]
    F. Micheli and J. Lygeros, “Scenario-based stochastic MPC for systems with uncertain dynamics,” in Proc. European Control Conf., London, UK, 2022, pp. 833–838.
    [16]
    T. Bai, S. Li, and Y. Zou, “Distributed MPC for reconfigurable architecture systems via alternating direction method of multipliers,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1336–1344, Jul. 2021. doi: 10.1109/JAS.2020.1003195
    [17]
    Mi X, Zou Y, Li S, et al, “Self-triggered DMPC design for cooperative multiagent systems,” IEEE Trans. Ind. Electron., vol. 6, no. 1, pp. 512–520, Feb. 2019.
    [18]
    J. Yin, D. Shen, X. Du, and L. Li, “Distributed stochastic model predictive control with taguchi’s robustness for vehicle platooning,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 15967–15979, Sept. 2022. doi: 10.1109/TITS.2022.3146715
    [19]
    M. Cannon, B. Kouvaritakis, S. V. Rakovic, and Q. F. Cheng, “Stochastic tubes in model predictive control with probabilistic constraints,” IEEE Trans. Autom. Control, vol. 56, no. 1, pp. 194–200, Jan. 2011. doi: 10.1109/TAC.2010.2086553
    [20]
    Y. Zou, X. Su, S. Li, Y. Niu, and D. Li, “Event-triggered distributed predictive control for asynchronous coordination of multi-agent systems,” Automatica, vol. 99, pp. 92–98, Jan. 2019. doi: 10.1016/j.automatica.2018.10.019
    [21]
    X. Mi and S. Li, “Event-triggered MPC design for distributed systems with network communications,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 240–250, Jan. 2018. doi: 10.1109/JAS.2016.7510154
    [22]
    Z. Shao, Y. Wang, Z. Li, and Y. Song, “Dynamic constraint-driven event-triggered control of strict-feedback systems without max/min values on irregular constraints,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 569–580, Mar. 2024. doi: 10.1109/JAS.2023.123804
    [23]
    F. Li and Y. Liu, “Event-triggered stabilization for continuous-time stochastic systems,” IEEE Trans. Autom. Control, vol. 65, no. 10, pp. 4031–4046, Oct. 2020. doi: 10.1109/TAC.2019.2953081
    [24]
    H. Li and Y. Shi, “Event-triggered robust model predictive control of continuous-time nonlinear systems,” Automatica, vol. 50, no. 5, pp. 1507–1513, May 2014. doi: 10.1016/j.automatica.2014.03.015
    [25]
    Q. Sun, J. Chen, and Y. Shi, “Integral-type event-triggered model predictive control of nonlinear systems with additive disturbance,” IEEE T. Cybern., vol. 51, no. 12, pp. 5921–5929, Dec. 2021. doi: 10.1109/TCYB.2019.2963141
    [26]
    M. Wang, J. Sun, and J. Chen, “Stabilization of perturbed continuous-time systems using event-triggered model predictive control,” IEEE T. Cybern., vol. 52, no. 5, pp. 4039–4051, May 2022. doi: 10.1109/TCYB.2020.3011177
    [27]
    H. Wang, J. Wang, H. Xu, and S. Zhao, “A self-triggered stochastic model predictive control for uncertain networked control system,” Int. J. Control, vol. 96, no. 8, pp. 2113–2123, Aug. 2023. doi: 10.1080/00207179.2022.2084163
    [28]
    S. Subramanian, M. Aboelnour, and S. Engell, “Robust tube-enhanced multi-stage output feedback MPC for linear systems with additive and parametric uncertainties,” in Proc. 18th European Control Conf., Naples, Italy, 2019, pp. 331–336.
    [29]
    E. Kofman, J. A. De Doná, and M. M. Seron, “Probabilistic set invariance and ultimate boundedness,” Automatica, vol. 48, no. 10, pp. 2670–2676, Oct. 2012. doi: 10.1016/j.automatica.2012.06.074
    [30]
    L. Hewing, K. P. Wabersich, and M. N. Zeilinger, “Recursively feasible stochastic model predictive control using indirect feedback,” Automatica, vol. 119, Sept. 2020.
    [31]
    S. V. Rakovic, E. C. Kerrigan, K. I. Kouramas, and D. Q. Mayne, “Invariant approximations of the minimal robust positively invariant set,” IEEE Trans. Autom. Control, vol. 50, no. 3, pp. 406–410, Mar. 2005. doi: 10.1109/TAC.2005.843854
    [32]
    M. Ono, “Joint chance-constrained model predictive control with probabilistic resolvability,” in Proc. American Control Conf., Montreal, Canada, 2012, pp. 435–441.
    [33]
    T. Hashimoto, “Probabilistic constrained model predictive control for linear discrete-time systems with additive stochastic disturbances,” in Proc. 52nd IEEE Conf. Decis. Control, Florence, Italy, 2013, pp. 6434–6439.
    [34]
    B. Kouvaritakis and M. Cannon, Model Predictive Control: Classical, Robust and Stochastic. Cham, Switzerland: Springer, 2016.
    [35]
    F. Lauro, L. Longobardi, and S. Panzieri, “An adaptive distributed predictive control strategy for temperature regulation in a multizone office building,” in Proc. IEEE Int. Workshop Intell. Energy Syst., San Diego, USA, 2014, pp. 32–37.
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