Data-driven robust model predictive control for polytopic uncertain systems under AF cooperation protocol

doi:10.1080/00207721.2026.2658157

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Data-driven robust model predictive control for polytopic uncertain systems under AF cooperation protocol

2026-04-15

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DOI：10.1080/00207721.2026.2658157

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Abstract

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To address the robust control challenges of unknown linear discrete-time systems subjected to persistent bounded disturbances and polytopic uncertainties, this paper first proposes a data-driven robust model predictive control (RMPC) framework based on the amplify-and-forward cooperation protocol (AFCP). In remote transmission environments where signals are prone to attenuation due to blockage, loss, and other impairments, the AFCP is innovatively introduced between sensor nodes and the remote controller node to effectively improve signal transmission quality. To tackle the challenge of unmeasurable system states, this work first integrates data-driven strategies into RMPC methods with the protocol. Historical input–output data are utilised to construct an equivalent state-space model without requiring explicit system identification. Based on this, a dynamic output feedback controller is designed, and a terminal constraint set (TCS) is formulated, breaking through the traditional reliance on known models. Furthermore, an infinite-horizon quadratic objective function is designed based on mathematical expectation to handle system uncertainties and stochastic disturbances. Combining singular value decomposition (SVD) techniques, the intractable original ‘min–max’ optimisation problem is transformed into a computable auxiliary online optimisation problem. Mean-square input-to-state stability of the closed-loop system is rigorously proven. Finally, simulation results validate the effectiveness and robustness of the proposed data-driven RMPC method in guaranteeing hard constraint satisfaction.

Keywords:

Data-driven model predictive control

mean-square input-state stability

amplify-and-forward cooperation protocol

persistent bounded disturbances