Model-Free Data-Driven Predictive Control Using Reinforcement Learning

Sawant, Shambhuraj; Reinhardt, Dirk; Kordabad, Arash Bahari; Gros, Sebastien

doi:10.1109/cdc49753.2023.10383431

Cited by 2 publications

(1 citation statement)

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“…Of interest for us are algorithmic schemes tailored specifically for DDMPC based on data-driven optimization algorithms, reinforcement learning-based control strategies, and adaptive learning algorithms: these computational schemes are designed to tackle the challenges posed by real-world data, ensuring robust and efficient control performance [17,18]. Performance evaluations and comparisons between DDMPC and traditional model-based control methods have been conducted: the idea is assess the advantages and limitations of DDMPC in terms of control quality, robustness, computational efficiency, and adaptability to changing environments (see [19]).…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Approach to Set-Theoretic Model Predictive Control for Nonlinear Systems

Giannini,

Famularo

2024

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In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controller within a data-driven context. To achieve this, we translate model-based conditions into data series of available input and output signals. This translation process leverages recent advances in data-driven control theory, enabling the controller to operate effectively without relying on explicit system models. The proposed framework incorporates a robust methodology for managing system constraints, ensuring that the control actions remain within predefined bounds. By means of time sequences, the controller learns the underlying system dynamics and adapts to changes in real time, providing enhanced performance and reliability. The integration of set-theoretic methods allows for the systematic handling of uncertainties and disturbances, which are common when the trajectory of a nonlinear system is embedded inside a linear trajectory state tube. To validate the effectiveness of our DDMPC framework, we conduct extensive simulations on a nonlinear DC motor system. The results demonstrate significant improvements in control performance, highlighting the robustness and adaptability of our approach compared to traditional model-based MPC techniques.

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Section: Introductionmentioning

confidence: 99%