Data-driven model predictive control: closed-loop guarantees and experimental results

Abstract

We provide a comprehensive review and practical implementation of a recently developed model predictive control (MPC) framework for controlling unknown systems using only measured data and no explicit model knowledge. Our approach relies on an implicit system parametrization from behavioral systems theory based on one measured input-output trajectory. The presented MPC schemes guarantee closed-loop stability for unknown linear time-invariant (LTI) systems, even if the data are affected by noise. Further, we extend this MPC framework to control unknown nonlinear systems by continuously updating the data-driven system representation using new measurements. The simple and intuitive applicability of our approach is demonstrated with a nonlinear four-tank system in simulation and in an experiment.

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Cited by in corpus (8)

• Linear tracking MPC for nonlinear systems Part II: The data-driven case
• An overview of systems-theoretic guarantees in data-driven model predictive control
• On a Stochastic Fundamental Lemma and Its Use for Data-Driven Optimal Control
• Towards data-driven stochastic predictive control
• Stability in data-driven MPC: an inherent robustness perspective
• AI-MOLE: Autonomous Iterative Motion Learning for Unknown Nonlinear Dynamics with Extensive Experimental Validation
• An Online Adaptation Strategy for Direct Data-driven Control
• Offline Uncertainty Sampling in Data-driven Stochastic MPC