Bayesian nonparametric approach to Adaptive Control using Gaussian processes

Girish Chowdhary, Hassan A. Kingravi, Jonathan P. How, Patricio A. Vela

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Most current Model Reference Adaptive Control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a-priori, often through expert judgment. Examples of such adaptive elements are the commonly used Radial Basis Function Networks (RBFNs) with pre-allocated centers allocated based on the expected operating domain. A severe limitation of such RBFN MRAC methods is that if the system operates outside of the expected operating domain, such adaptive elements can become non-effective, thus rendering the adaptive controller only semi-global in nature. This paper treats system uncertainties as distributions over functions and proposes Gaussian Process based adaptive elements. We show that these Bayesian nonparametric adaptive elements guarantee good closed loop performance with minimal prior domain knowledge of the uncertainty through stochastic stability arguments. Online implementable GP inference method are evaluated in simulations and compared with RBFN adaptive controllers with pre-allocated centers. The results indicate that GP-MRAC overcomes the limitations of MRAC employing RBFN with fixed parameters.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781467357173
StatePublished - 2013
Externally publishedYes
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216


Other52nd IEEE Conference on Decision and Control, CDC 2013

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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