Data-Driven Min-Max MPC for Linear Systems: Robustness and Adaptation

2024/04/29 by Yifan Xie, Julian Berberich, Xie, Yifan +3 · 3 citations
Engineering · #Advanced Control Systems Optimization #Advanced Memory and Neural Computing #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2404.19096

openalex publication_date 2024/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Data-driven controllers design is an important research problem, in particular when data is corrupted by the noise. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme using noisy input-state data for unknown linear time-invariant (LTI) system. The unknown system matrices are characterized by a set-membership representation using the noisy input-state data. Leveraging this representation, we derive an upper bound on the worst-case cost and determine the corresponding optimal state-feedback control law through a semidefinite program (SDP). We prove that the resulting closed-loop system is robustly stabilized and satisfies the input and state constraints. Further, we propose an adaptive data-driven min-max MPC scheme which exploits additional online input-state data to improve closed-loop performance. Numerical examples show the effectiveness of the proposed methods.

Cited by

• Robust Data-Driven Receding Horizon Control
• On Model Predictive Funnel Control with Equilibrium Endpoint Constraints
• Data-driven Internal Model Control for Output Regulation

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