Robust event‐triggered model predictive control for constrained linear continuous system

Luo, Yu; Xia, Yuanqing; Sun, Zhongqi

doi:10.1002/rnc.4432

Cited by 45 publications

(52 citation statements)

References 29 publications

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“…In their design, the feasibility condition is limited by the bound of disturbance and the prediction horizon primarily, while the stability is affected by the bound of disturbance, the prediction horizon, and the threshold. Luo et al 24 developed a dual‐mode control strategy and discussed the effects of the inter‐event time and the bound of disturbance on control performance. As system state variables cannot be all measured accurately, Tang et al 25 designed a multistep event‐triggered output feedback MPC for Takagi–Sugeno (T‐S) fuzzy systems, which is designed to release the current system output when the event scheme is triggered.…”

Section: Introductionmentioning

confidence: 99%

A novel linear matrix inequality‐based robust event‐triggered model predictive control for a class of discrete‐time linear systems

Ding

Peng

et al. 2021

Intl J Robust & Nonlinear

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This paper studies a linear matrix inequality (LMI)‐based robust event‐triggered model predictive control (ET‐MPC) for a class of discrete linear time‐invariant systems subject to bounded disturbances. In the presented robust event‐triggered MPC, the event‐triggered mechanism is first set by the deviation between the optimal state and actual state. In addition, the Lyapunov‐based stability condition is employed in the constraints for simplifying parameters and enhancing the universality. Subsequently, a novel design framework that involves LMIs approach is developed. In such a framework, the Lyapunov weight matrix is predesigned by solving a convex optimization problem with LMIs and the infinite horizon MPC optimization problem is also transformed as LMIs such that the computational complexity is significantly reduced. To guarantee robust constraint satisfaction, a dual‐mode control strategy is adopted. In this way, the proposed robust ET‐MPC has the ability to deal with the constrained system. Furthermore, the theoretical analysis of the recursive feasibility and stability is provided. The numerical simulations and comparison studies demonstrate that the proposed robust ET‐MPC not only has satisfying control performance but also significantly reduces the computational burden.

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Section: Introductionmentioning

confidence: 99%

A novel linear matrix inequality‐based robust event‐triggered model predictive control for a class of discrete‐time linear systems

Ding

Peng

et al. 2021

Intl J Robust & Nonlinear

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show abstract

“…Meanwhile, many results of the ETMPC scheme have also been proposed for the continuous systems in which the system performance and constraints can be satisfied all the time instead of only at the discrete instants for discrete systems 17 . A robustness constraint approach was proposed for constrained continuous nonlinear systems in Reference 18 and extended to event‐triggered robust MPC in References 10,19. Reference 17 took advantage of the tightened state constraint 20 and removed the requirement of the prediction horizon for guaranteeing the feasibility in Reference 10.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered model predictive control for disturbed linear systems under two‐channel transmissions

Hao

et al. 2020

Intl J Robust & Nonlinear

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This article studies an event-triggered model predictive control problem for constrained continuous-time linear systems subject to bounded disturbances. Two different event-triggered strategies are constructed in the sensor and the controller nodes for reducing the communication and computational loads, respectively. The continuous predicted control trajectory generated by the controller is applied to the plant under a sample-and-hold implementation. By constructing a feasible control sequence, the sufficient conditions are derived to guarantee the feasibility and stability of the closed-loop system. Furthermore, the case of multiple samples within an event-triggered control update interval is considered. It is shown that a larger number of samples will improve the triggering performance while increasing the amount of transmission information. Finally, a simulation example is provided to show the feasibility and the effectiveness of the proposed strategy.

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“…It was shown that such a consideration revealed a simple trigger condition based on the input-to-state stability (ISS) of the closed-loop control system. In Luo et al (2018), a dual controller was designed based on an event-triggered MPC, and a state-feedback control was developed for a continuous linear time-invariant system subject to control input constraints and bounded disturbances. In Shi et al (2019), a networked sampled-data robust MPC was proposed by considering the induced network–delay as well as input saturation.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered robust model predictive control for Lipschitz nonlinear networked control systems subject to communication delays

Ghorbani

Safavi

Naghavi³

2020

Transactions of the Institute of Measurement and Control

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In this paper, the problem of event-triggered robust model predictive control (MPC) was examined for a class of Lipchitz nonlinear networked control systems (NCS) with network-induced delays and subject to external disturbances. An event-triggering scheme for a continuous-time NCS was proposed, which reduced the communication traffic and computational burden of the MPC algorithm simultaneously. In comparison with the existing event-triggered nonlinear MPC (NMPC) approaches, the controller in this paper was designed as a state feedback control law, which minimized a “worst-case” performance index over an infinite horizon subject to constraints on the control input. The controller and event generator parameters were developed as a convex optimization problem, encompassing some linear matrix inequalities (LMIs). Simulation results showed that the proposed event-triggering NMPC scheme preserved closed-loop performance while reducing the communication rate and the computational time.

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Robust event‐triggered model predictive control for constrained linear continuous system

Cited by 45 publications

References 29 publications

A novel linear matrix inequality‐based robust event‐triggered model predictive control for a class of discrete‐time linear systems

A novel linear matrix inequality‐based robust event‐triggered model predictive control for a class of discrete‐time linear systems

Event‐triggered model predictive control for disturbed linear systems under two‐channel transmissions

Event-triggered robust model predictive control for Lipschitz nonlinear networked control systems subject to communication delays

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