do-mpc: Towards FAIR nonlinear and robust model predictive control

Over the last decades, model predictive control (MPC) has shown outstanding performance for control tasks from various domains. This performance has further improved in recent years with advanced MPC schemes for nonlinear systems under uncertainty including economic control objectives. These recent...

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Veröffentlicht in:Control engineering practice Jg. 140; S. 105676
Hauptverfasser: Fiedler, Felix, Karg, Benjamin, Lüken, Lukas, Brandner, Dean, Heinlein, Moritz, Brabender, Felix, Lucia, Sergio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2023
Schlagworte:
Learning-based control
Nonlinear model predictive control
Robust control
Learning-based control
Robust control
Nonlinear model predictive control
ISSN:0967-0661, 1873-6939
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Zusammenfassung:Over the last decades, model predictive control (MPC) has shown outstanding performance for control tasks from various domains. This performance has further improved in recent years with advanced MPC schemes for nonlinear systems under uncertainty including economic control objectives. These recent improvements often fail to bridge the gap between MPC researchers and control practitioners in academia and industry, where classical control approaches and traditional linear MPC still dominate most applications. This is despite the fact that advanced MPC controllers can lead to significant energy savings, yield improvements, safer operation and other benefits. In this work, we identify four main obstacles hindering the widespread adoption of advanced MPC methods. These are the unavailability of models, the challenges associated with deploying complex controllers on physical systems, the scarcity of rapid prototyping tools for advanced methods and the limited reproducibility and reusability of advanced MPC controllers and their results. We find that the FAIR principles (findable, accessible, interoperable, reusable) for scientific data-management and research software can play an important role in tackling these obstacles. Following these guidelines, we discuss FAIR solutions and present the open-source software do-mpc as a concrete implementation. The presented solutions include interoperability with neural network toolboxes to simplify nonlinear system identification, interoperability with the OPC UA communication protocol for deployment, and a reproducible data-sampling framework for transparent controller validation, system identification and approximate MPC. The potential of the proposed solutions is illustrated with several simulation studies. •Analysis of obstacles hindering the widespread adoption of advanced MPC.•Discussion of solutions to overcome these obstacles considering the FAIR principles.•Introduction of do-mpc, an advanced MPC software, implementing the FAIR solutions.•Comparison of do-mpc with other tools under consideration of the FAIR principles.•Demonstration of FAIR solutions in several simulation studies.
ISSN:0967-0661
1873-6939
DOI:10.1016/j.conengprac.2023.105676