Stable Gaussian process based tracking control of Lagrangian systems

Thomas Beckers, Jonas Umlauft, Dana Kulic, Sandra Hirche

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

8 Citations (Scopus)


High performance tracking control can only be achieved if a good model of the dynamics is available. However, such a model is often difficult to obtain from first order physics only. In this paper, we develop a data-driven control law that ensures closed loop stability of Lagrangian systems. For this purpose, we use Gaussian Process regression for the feedforward compensation of the unknown dynamics of the system. The gains of the feedback part are adapted based on the uncertainty of the learned model. Thus, the feedback gains are kept low as long as the learned model describes the true system sufficiently precisely. We show how to select a suitable gain adaption law that incorporates the uncertainty of the model to guarantee a globally bounded tracking error. A simulation with a robot manipulator demonstrates the efficacy of the proposed control law.

Original languageEnglish
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control (CDC 2017)
EditorsMario Sznaier
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781509028733, 9781509028726
ISBN (Print)9781509028740
Publication statusPublished - 2017
Externally publishedYes
EventIEEE Conference on Decision and Control 2017 - Melbourne Convention Center, Melbourne, Australia
Duration: 12 Dec 201715 Dec 2017
Conference number: 56th (Proceedings)


ConferenceIEEE Conference on Decision and Control 2017
Abbreviated titleCDC 2017
Internet address

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