Robust event-triggered model predictive control of polytopic LPV systems: An input-to-state stability approach

This paper addresses the problem of designing robust event-triggered tube-based model predictive control (TMPC) for constrained linear parameter-varying systems. The main challenge with implementing TMPC schemes in an event-triggered setup is that the assumption of feedback for all sampling times is not satisfied, which leads to an exponential growth of the predicted sets. First, a novel modification of homothetic TMPC is introduced to consider the effect of open-loop control between triggering times. At each sampling instant, the TMPC provides the maximal feasible number of open-loop steps and a sequence of control inputs, which can be applied in an open-loop fashion while satisfying the state and control constraints. Second, depending on the maximal number of open-loop steps, the triggering condition is derived based on the input-to-state stability (ISS) concept with the aim to reduce the communication traffic between sensors, controllers and actuators. Moreover, recursive feasibility and asymptotic stability of the closed-loop system are guaranteed. Simulation results demonstrate the efficacy of the proposed scheme while reducing the frequency of the update times. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper addresses the problem of designing robust event-triggered tube-based model predictive control (TMPC) for constrained linear parameter-varying systems. The proposed method introduces a novel modification of homothetic TMPC to consider the effect of open-loop control between triggering times, and derives the triggering condition based on the input-to-state stability (ISS) concept to reduce communication traffic. The simulation results demonstrate the effectiveness of the proposed scheme while reducing the update frequency.