Integral-Type Event-Triggered Model Predictive Control of Nonlinear Systems With Additive Disturbance

Abstract

This paper studies the event-triggered receding horizon control (RHC) of continuous-time nonlinearsystems. An integral-type event-triggered mechanism is proposed to save the communicational resource, and a less conservative robustness constraint is introduced to the RHC scheme for compensating the additive disturbance. Based on these formulations, the designed event-triggered algorithm is shown to have better performance on avoiding unnecessary communication. Furthermore, the feasibility of the integral-type event-triggered RHC scheme and the stability of the closed-loop system are rigorously investigated. Several sufficient conditions to guarantee these properties are established, which indicate that a trade-off exists for designing parameters such as the prediction horizon, the disturbance bound, the triggering level, and the contraction rate for the robustness constraint. The effectiveness of the proposed algorithm is illustrated by a numerical example.

Event-triggered robust MPC of nonlinear cyber-physical systems against DoS attacks

“…(1) A new robustness constraint is designed for the MPC optimization problem in order to tackle additive disturbance. Different from the existing techniques [14,15], the proposed robustness constraint is constructed based on the state constraint set rather than the terminal state constraint, which can bring the additional benefit of being able to act as state constraints.…”

Self-Triggered Model Predictive Control for Perturbed Underwater Robot Systems

“…e former is performed by constantly monitoring the state of the system to determine when control actions must be triggered, while the latter is performed by determining the triggering interval and the next trigger time based on the predicted system states. At present, event-triggered model predictive control strategies are mainly studied for two types of systems: unperturbed systems [7][8][9] and perturbed systems [10][11][12][13][14][15]. Similarly, the self-triggered MPC strategies have been investigated in [16][17][18] and so on.…”

“…In [28], a new distributed control method based on acceleration gradient method and decoupling strategy was proposed to solve constrained MPC problems for systems composed of nonlinear subsystems. In [15], for nonlinear systems with additional perturbations, an integral-based event-triggered MPC framework was provided to determine the selection of triggering conditions. In [29], the authors proposed an event-based control strategy for the nonlinear distributed systems, which can release the burden of the network communication while achieving the desired global performance.…”

Aperiodic event‐triggered robust model predictive control for linear parameter‐varying system with round‐robin protocol

“…[37][38][39][40] Depending on the full or partial availability of system states, the event-triggered MPC can be further classified into: the state feedback event-triggered MPC and the output feedback event-triggered MPC. Specially, Sun et al 41 studied an integral-type event-triggered MPC for the nonlinear system with disturbance, where the triggering condition was designed on the integral of errors between the real measured and optimal predicted state sequences. More recently, a similar work on state feedback event-triggered MPC can be found in Wang et al, 42 but the authors mainly addressed the input-to-state stability for the nonlinear system with disturbance.…”