Dynamic output-feedback RMPC for systems with polytopic uncertainties under Round-Robin protocol

摘要

This paper is concerned with the dynamic output-feedback robust model predictive control (RMPC) problem for systems with polytopic uncertainties under the Round-Robin (RR) protocol. In the backward channel, i.e., from the sensors to the controller, several sensors share a communication network to transmit the data to the remote controller, and thus data collision might happen if these sensors start transmissions together. In order to prevent data from collisions, a so-called RR protocol is utilized to orchestrate the data transmission order, where only one node with token is allowed to send data at each transmission instant. The aim of the problem addressed is to design a set of controllers in the framework of dynamic output-feedback RMPC (OFRMPC) so as to guarantee the asymptotical stability of the closed-loop system in terms of the token-dependent Lyapunov-like approach. By taking the influence of the underlying RR protocol into consideration, sufficient conditions with less conservatism are obtained by solving a time-varying terminal constraint set of an auxiliary optimization problem. Furthermore, an algorithm including both off-line and online parts is provided to find a sub-optimal solution. Finally, a numerical simulation result is exploited to illustrate the usefulness and effectiveness of the proposed RMPC strategy. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.