Zusammenfassung
Die effiziente Lösung von Optimierungsproblemen in Echtzeit bildet die Grundlage vieler moderner Regelungs- und Schätzverfahren. So basieren die prädiktive Regelung sowie die Zustandsschätzung auf bewegtem Horizont auf der wiederholten Lösung beschränkter Optimierungsprobleme. Methodische sowie technologische Fortschritte ermöglichen den Einsatz optimierungsbasierter Verfahren in Echtzeit selbst für anspruchsvolle Regelungs- und Schätzprobleme mit Zeitkonstanten im Bereich von Mikro- oder sogar Nanosekunden. Nach einem Überblick über effiziente Lösungsansätze für die echtzeitfähige Umsetzung optimierungsbasierter Schätz- und Regelungsverfahren fokussiert sich diese Arbeit auf wesentliche technologische, regelungs- und systemtheoretische Aspekte für deren erfolgreichen Einsatz.
Abstract
The efficient real-time solution of optimization problems is at the core of many modern control and estimation approaches. Examples are predictive control methods and moving horizon-based state and parameter estimation approaches subject to constraints, which require the repeated solution of constrained optimization problems in real-time. Algorithmic as well as technological advancements nowadays allow the real-time use of optimization-based control and estimation approaches even for challenging problems with time constants in the range of micro- or even nanoseconds. The first part of this work provides a review of efficient numerical methods for the real-time implementation of optimization-based estimation and control approaches. The second part focuses on important technological, control, and system theoretical aspects, which need to be taken into account for a successful implementation of embedded optimization methods in control.
About the authors
Prof. Dr.-Ing. Rolf Findeisen leitet den Lehrstuhl Systemtheorie und Regelungstechnik der Otto-von-Guericke Universität Magdeburg. Arbeitsgebiete: prädiktive Regelung, autonome System, Maschinelles Lernen und Regelung, Regelung vernetzter Systeme, mit Anwendungen in der Mechatronik/Robotik, dem autonomen Fahren, Biotechnologie, Medizin.
Prof. Dr.-Ing. Knut Graichen ist Professor für Mess- und Regelungstechnik an der Universität Ulm. Arbeitsgebiete: optimale und modellprädiktive Regelung, nichtlineare Steuerungs- und Regelungsverfahren, eingebettete Umsetzung von optimierungsbasierten Verfahren für mechatronische und vernetzte Systeme.
Prof. Dr.-Ing. Martin Mönnigmann ist Leiter des Lehrstuhls für Regelungstechnik und Systemtheorie an der Ruhr-Universität Bochum. Arbeitsgebiete: modellprädiktive Regelung, robuste Optimierung dynamischer Systeme, Regelung energie- und verfahrenstechnischer Systeme.
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