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

Abstract

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.

Event‐triggered MPC for linear systems with bounded disturbances: An accumulated error based approach

“…In this section, two examples are given to verify the effectiveness of the proposed algorithm.Example We consider the following linearized cart‐damper‐spring system [21, 32]: $$\begin{equation} \dot{x}(t)= \def\eqcellsep{&}\begin{bmatrix} 0 &\quad 1 \\[4pt] -k/M_c &\quad -\zeta /M_c \end{bmatrix}x(t) + \def\eqcellsep{&}\begin{bmatrix} 0 \\[4pt] 1/M_c \end{bmatrix}u(t) + \def\eqcellsep{&}\begin{bmatrix} 1 \\[4pt] 1 \end{bmatrix} \omega (t), \end{equation}$$where $$M_c = 1.5 \mathrm{kg}$$ is the mass of the cart, $$k = 0.25 \mathrm{N/m}$$ is the linear spring factor, $$\zeta = 0.42 \mathrm{N \cdot s/m}$$ is the damper factor. The input constraint is $$-0.25 \le u(t) \le 0.25$$, and $$\omega$$ is a bounded additive disturbance by $$\Vert \omega \Vert \le \mu$$.…”

Event‐based optimal filter for a networked system with multiplicative and auto/cross‐correlated process and measurement noise

“…Further, convergence of optimal estimator for discrete time linear systems with additive process and measurement noise and autocorrelated multiplicative measurement noise. 20 However, it did not consider the effect of delay and dropout. In Reference 21, an estimator is designed for system affected by auto correlated process and measurement noise and correlated multiplicative noise in both state and measurement matrix.…”

“…The additive process noise is finite step autocorrelated but uncorrelated with measurement noise. Further, convergence of optimal estimator for discrete time linear systems with additive process and measurement noise and autocorrelated multiplicative measurement noise 20 . However, it did not consider the effect of delay and dropout.…”

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

“…Currently, the event-triggered MPC has received a lot of research efforts in the literature. [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.…”