Robust model predictive control with aperiodic actuation

Bregman, Sander; Kolarijani, Arman Sharifi; Keviczky, Tamás

doi:10.1109/cdc.2017.8264467

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, a fair comparison between the performances of the ET and standard implementations of the RMPC method becomes possible. This paper extends the results of the authors' previous work in [19] in multiple directions, in particular, by simplifying the triggering "law." The approach in [19] requires an "advanced" triggering mechanism that is responsible for: 1) constructing certain input and state sequences; 2) evaluating the satisfaction of MPC's constraints by these sequences; and 3) comparing the values of the cost function based on the constructed sequences with the value function at the last triggering instance.…”

Section: Introductionsupporting

confidence: 72%

A Decentralized Event-Based Approach for Robust Model Predictive Control

Kolarijani

Bregman

Esfahani

et al. 2020

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

In this paper, we propose an event-based sampling policy to implement a constraint-tightening, robust MPC method. The proposed policy enjoys a computationally tractable design and is applicable to perturbed, linear time-invariant systems with polytopic constraints. In particular, the triggering mechanism is suitable for plants with no centralized sensory node as the triggering mechanism can be evaluated locally at each individual sensor. From a geometrical viewpoint, the mechanism is a sequence of hyperrectangles surrounding the optimal state trajectory such that robust recursive feasibility and robust stability are guaranteed. The design of the triggering mechanism is cast as a constrained parametric-in-set optimization problem with the volume of the set as the objective function. Reparameterized in terms of the set vertices, we show that the problem admits a finite tractable convex program reformulation and a linear program relaxation. Several numerical examples are presented to demonstrate the effectiveness and limitations of the theoretical results.

show abstract

Section: Introductionsupporting

confidence: 72%