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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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