Adaptively robust nonlinear model predictive control based on attack identification
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Sarah Braun
Sarah Braun received the M. Sc. degree in Mathematics from Technische Universität München, Germany, in 2018. She is currently pursuing a PhD at Technische Universität Berlin and is a member of a research group on autonomous systems and control at Siemens Technology, Munich, Germany. Her research interests include numerical methods for nonlinear optimization and control, robust model predictive control, and attack identification in dynamic systems.
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Sebastian Albrecht
Sebastian Albrecht joined Siemens Technology in 2015 as a Research Scientist addressing topics from robotics, autonomous systems and control in Munich, Germany. Since 2014 he holds a PhD in Mathematics from Technische Universität München, Germany. His main research interests are numerical methods for nonlinear optimization and control and their application to challenging real-world problems.
Sergio Lucia received the M. Sc. degree in electrical engineering from the University of Zaragoza, Spain, in 2010, and the Dr. Ing. degree in optimization and automatic control from TU Dortmund University, Germany, in 2014. He joined the Otto-von-Guericke Universität Magdeburg and visited the Massachusetts Institute of Technology as a Postdoctoral Fellow. He was an Assistant Professor at TU Berlin between 2017 and 2020. Since 2020, he has been a Professor at TU Dortmund University and head of the Laboratory of Process Automation Systems. His research interests include decision-making under uncertainty, distributed control, and the interplay between machine learning and control theory.
Abstract
Robust model predictive control (MPC) is an essential tool for control systems under uncertainty as it allows for constraint satisfaction even if disturbances occur. When a system suffers malicious attacks, in contrast to parametric uncertainties or known systems faults, it is difficult to specify tight uncertainty ranges within which possible disturbances lie. In this case, very conservative solutions or even infeasible problems are obtained. To address this issue, we propose an adaptively robust MPC scheme that adjusts the uncertainty ranges to available knowledge about the attackers. To this end, we combine a recently proposed method identifying unknown attacks on nonlinear systems with a multi-stage approach to robust MPC. We illustrate the potential of the method in a numerical case study with a distributed nonlinear system.
Zusammenfassung
Robuste modellprädiktive Regelung (MPC) ist ein wichtiges Werkzeug für Systeme mit Unsicherheit, da sie die Einhaltung von Nebenbedingungen auch im Störungsfall ermöglicht. Ist ein System bösartigen Angriffen ausgesetzt, ist es im Gegensatz zu parametrischen Unsicherheiten oder bekannten Systemfehlern aber schwierig, enge Unsicherheitsbereiche festzulegen, in denen mögliche Störungen liegen. Fehlen diese, erhält man sehr konservative Lösungen oder gar unzulässige Probleme. Deshalb stellen wir ein adaptiv robustes MPC-Schema vor, das die Unsicherheitsbereiche an verfügbares Wissen über die Angreifer anpasst. Dazu kombinieren wir eine kürzlich vorgestellte Identifikationsmethode für unbekannte Angriffe auf nichtlineare Systeme mit einem mehrstufigen Ansatz für robustes MPC. Eine Fallstudie mit einem verteilten nichtlinearen System zeigt das Potential der Methode.
Funding source: Bundesministerium für Bildung und Forschung
Award Identifier / Grant number: 01S18066B
Funding statement: This work was supported by the German Federal Ministry of Education and Research (BMBF) via the funded research project AlgoRes (01S18066B).
About the authors
Sarah Braun received the M. Sc. degree in Mathematics from Technische Universität München, Germany, in 2018. She is currently pursuing a PhD at Technische Universität Berlin and is a member of a research group on autonomous systems and control at Siemens Technology, Munich, Germany. Her research interests include numerical methods for nonlinear optimization and control, robust model predictive control, and attack identification in dynamic systems.
Sebastian Albrecht joined Siemens Technology in 2015 as a Research Scientist addressing topics from robotics, autonomous systems and control in Munich, Germany. Since 2014 he holds a PhD in Mathematics from Technische Universität München, Germany. His main research interests are numerical methods for nonlinear optimization and control and their application to challenging real-world problems.
Sergio Lucia received the M. Sc. degree in electrical engineering from the University of Zaragoza, Spain, in 2010, and the Dr. Ing. degree in optimization and automatic control from TU Dortmund University, Germany, in 2014. He joined the Otto-von-Guericke Universität Magdeburg and visited the Massachusetts Institute of Technology as a Postdoctoral Fellow. He was an Assistant Professor at TU Berlin between 2017 and 2020. Since 2020, he has been a Professor at TU Dortmund University and head of the Laboratory of Process Automation Systems. His research interests include decision-making under uncertainty, distributed control, and the interplay between machine learning and control theory.
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© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Ausgewählte Beiträge des GMA Fachausschusses 1.50
- Survey
- A compendium of optimization algorithms for distributed linear-quadratic MPC
- Rollout event-triggered control: reconciling event- and time-triggered control
- Methoden
- Uncertain AoI in stochastic optimal control of constrained LTI systems
- Gradient-Tracking-basierte Lösung von Multi-Cluster-Spielen
- Adaptively robust nonlinear model predictive control based on attack identification
- Cooperative transportation: realizing the promises of robotic networks using a tailored software/hardware architecture
- Fahrsituationsabhängiger Wechsel der Kommunikationstopologie für heterogene Platoons
- Dissertationen
- Automatisierte Erzeugung von Modellen und Beobachtern für physikalisch vernetzte Systeme
Articles in the same Issue
- Frontmatter
- Editorial
- Ausgewählte Beiträge des GMA Fachausschusses 1.50
- Survey
- A compendium of optimization algorithms for distributed linear-quadratic MPC
- Rollout event-triggered control: reconciling event- and time-triggered control
- Methoden
- Uncertain AoI in stochastic optimal control of constrained LTI systems
- Gradient-Tracking-basierte Lösung von Multi-Cluster-Spielen
- Adaptively robust nonlinear model predictive control based on attack identification
- Cooperative transportation: realizing the promises of robotic networks using a tailored software/hardware architecture
- Fahrsituationsabhängiger Wechsel der Kommunikationstopologie für heterogene Platoons
- Dissertationen
- Automatisierte Erzeugung von Modellen und Beobachtern für physikalisch vernetzte Systeme
