Robust Model Predictive Control for Polytopic Uncertain Systems with Amplify-and-Forward Relays Under Round-Robin Protocol

This paper addresses the robust model predictive control (RMPC) problem for polytopic uncertain systems with amplify-and-forward (AF) relays under round-robin (RR) protocol. To mitigate data collisions, the RR protocol schedules transmissions by allowing only one sensor node to send data at each time instant. Stochastic transmission power allocation is adopted to enhance remote signal reliability under limited channel capacity. A novel objective function is formulated to account for both polytopic parameter uncertainties and the randomness of AF relays. Sufficient conditions are derived through online optimization to guarantee the stability of closed-loop system. Numerical simulations validate the effectiveness of the proposed RMPC algorithm.

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Publication Details

Published
2025-05-23
DOI
https://doi.org/10.1109/etae65337.2025.11089545
Primary Topic
Advanced Control Systems Optimization
Type
article
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article

Robust Model Predictive Control for Polytopic Uncertain Systems with Amplify-and-Forward Relays Under Round-Robin Protocol

Jianhua Wang, Jiawei Zuo, Yanye Yu
Advanced Control Systems Optimization
article

Robust Model Predictive Control for Polytopic Uncertain Systems with Amplify-and-Forward Relays Under Round-Robin Protocol

Jianhua Wang, Jiawei Zuo, Yanye Yu
article en

Abstract

This paper addresses the robust model predictive control (RMPC) problem for polytopic uncertain systems with amplify-and-forward (AF) relays under round-robin (RR) protocol. To mitigate data collisions, the RR protocol schedules transmissions by allowing only one sensor node to send data at each time instant. Stochastic transmission power allocation is adopted to enhance remote signal reliability under limited channel capacity. A novel objective function is formulated to account for both polytopic parameter uncertainties and the randomness of AF relays. Sufficient conditions are derived through online optimization to guarantee the stability of closed-loop system. Numerical simulations validate the effectiveness of the proposed RMPC algorithm.

Huzhou Normal University (CN)
Openalex Percentile: Top 76%
Advanced Control Systems Optimization
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