A novel event-triggered robust MPC scheme for linear systems

An event‐triggered robust model predictive control (MPC) approach is proposed for linear discrete‐time systems with bounded disturbances. According to the probability distribution of bounded disturbances, an event‐triggered scheme involving a designed minimal robust positively invariant set is constructed to generate dynamic triggering sets. The MPC‐related optimization problem subject to hard constraints should be solved only at event‐triggered instants when the state is outside the corresponding triggering set. A classical tube‐based MPC that allows the initially predicted state different from the current actual state of the plant is considered to improve the feasible region. The designed event‐triggered controller can achieve a prescribed expectation of inter‐execution times and reduce the burden of communication and computation, while not sacrificing the quadratic performance significantly. It is proved that the proposed control approach ensures recursive feasibility and robust stability. Three examples are used to demonstrate the effectiveness and advantages of the proposed method.

Section: B Transient Behavior Analysismentioning

confidence: 99%

Section: B Transient Behavior Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Event‐triggered robust model predictive control for linear discrete‐time systems with a guaranteed average inter‐execution time

Deng

Shu

Chen

2022

“…In the field of discrete‐time systems concerning robust ET‐MPC, Zou et al 34 proposed a novel event‐triggered distributed MPC (ET‐DMPC) for discrete‐time multi‐agent systems and introduced a dynamic event‐triggering mechanism to further save computation resources. In addition, Darup et al 35 proposed an ET‐MPC dealt with constrained discrete‐time systems, which combines tube MPC and explicit MPC to further reduce the computational burden. Nevertheless, the feasibility was not guaranteed.…”

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

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.

“…Combined with event-triggered mechanism, robust MPC can obtain satisfactory performance and reduce computational load compared with conventional MPC. [19][20][21] In the work of Ferrara et al, 22 an efficient control framework for freeway systems was developed based on MPC, and the control law was updated when the considered error exceeded a predefined threshold. The focus of Lehmann et al 7 lied in ETMPC for discrete-time linear systems, where robust feasibility and stability were discussed in detail.…”

Section: Introductionmentioning

confidence: 99%

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

Luo

Xia

Sun

2018

Model predictive control (MPC) is capable to deal with multiconstraint systems in real control processes; however, the heavy computation makes it difficult to implement. In this paper, a dual-mode control strategy based on event-triggered MPC (ETMPC) and state-feedback control for continuous linear time-invariant systems including control input constraints and bounded disturbances is developed. First, the deviation between the actual state trajectory and the optimal state trajectory is computed to set an event-triggered mechanism and reduce the computational load of MPC. Next, the dual-mode control strategy is designed to stabilize the system. Both recursive feasibility and stability of the strategy are guaranteed by constructing a feasible control sequence and deducing the relationship of parameters, especially the inter-event time and the upper bound of the disturbances. Finally, the theoretical results are supported by numerical simulation. In addition, the effects of the parameters are discussed by simulation, which gives guidance to balance computational load and control performance. KEYWORDSbounded disturbances, continuous LTI system, event-triggered, input constraints, model predictive control 1216