Abstract

In this paper, we present a new architecture for Gaussian Processes Model Reference Adaptive Control (GP-MRAC) trained using a generative network. GP-MRAC is a successful method for achieving global performance in the systems enabling adaptive control. GP-MRAC can handle a broader set of uncertainties without requiring apriori knowledge of the domain of operation. However, existing GP-MRAC work requires estimates of the state-derivate, and this is a primary limitation in the implementation of the controller. In this paper, we alleviate this major limitation by creating Model reference adaptive framework as Generative Network (MRGeN). Our contribution is a generative network architecture for learning Gaussian model to predict system uncertainties without having to estimate the state derivatives while ensuring that the system stability properties are unaffected. We retain the nonparametric nature of the controller by sharing the kernels between GP's and MRGeN, ensuring global performance and stability guarantees. GP-MRGeN can also be viewed as a method of baseline policy transfers, with applications in Reinforcement Learning.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages237-243
Number of pages7
ISBN (Electronic)9781538613955
DOIs
StatePublished - Jan 18 2019
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018Dec 19 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2018-December
ISSN (Print)0743-1546

Conference

Conference57th IEEE Conference on Decision and Control, CDC 2018
CountryUnited States
CityMiami
Period12/17/1812/19/18

ASJC Scopus subject areas

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

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