Contraction L1-Adaptive Control using Gaussian Processes

Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos A. Theodorou

Research output: Contribution to journalConference articlepeer-review


We present a control framework that enables safe simultaneous learning and control for systems subject to uncertainties. The two main constituents are contraction theory-based L1-adaptive (CL1) control and Bayesian learning in the form of Gaussian process (GP) regression. The CL1 controller ensures that control objectives are met while providing safety certificates. Furthermore, the controller incorporates any available data into GP models of uncertainties, which improves performance and enables the motion planner to achieve optimality safely. This way, the safe operation of the system is always guaranteed, even during the learning transients.

Original languageEnglish (US)
Pages (from-to)1027-1040
Number of pages14
JournalProceedings of Machine Learning Research
StatePublished - 2021
Event3rd Annual Conference on Learning for Dynamics and Control, L4DC 2021 - Virtual, Online, Switzerland
Duration: Jun 7 2021Jun 8 2021


  • Adaptive Control
  • Gaussian Process Regression
  • Planning
  • Safe Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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