Adaptive-optimal control of constrained nonlinear uncertain dynamical systems using concurrent learning model predictive control

Maximilian Mühlegg, Girish Chowdhary, Jonathan P. How, Florian Holzapfel

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

Abstract

A concurrent learning adaptive-optimal control architecture for constrained aerospace systems with fast dynamics is presented. Exponential convergence properties of concurrent learning adaptive controllers are leveraged to guarantee a verifiable learning rate while guaranteeing stability in presence of significant modeling uncertainty. Radial Basis Function based adaptive elements are incorporated to approximate the uncertainty. The architecture switches to online-learned model based Model Predictive Control after an online automatic switch gauges the confidence in parameter estimates. A new switching metric ensures that the control architecture only switches to the model-based optimal controller if the uncertainty is approximated over the whole neural network operating domain. To achieve this a novel point selection algorithm for concurrent learning is presented. Numerical simulations on a wing-rock problem establish the effectiveness of the architecture.

Original languageEnglish (US)
Title of host publicationAIAA Guidance, Navigation, and Control (GNC) Conference
StatePublished - Sep 16 2013
Externally publishedYes
EventAIAA Guidance, Navigation, and Control (GNC) Conference - Boston, MA, United States
Duration: Aug 19 2013Aug 22 2013

Publication series

NameAIAA Guidance, Navigation, and Control (GNC) Conference

Other

OtherAIAA Guidance, Navigation, and Control (GNC) Conference
CountryUnited States
CityBoston, MA
Period8/19/138/22/13

ASJC Scopus subject areas

  • Aerospace Engineering
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Mühlegg, M., Chowdhary, G., How, J. P., & Holzapfel, F. (2013). Adaptive-optimal control of constrained nonlinear uncertain dynamical systems using concurrent learning model predictive control. In AIAA Guidance, Navigation, and Control (GNC) Conference (AIAA Guidance, Navigation, and Control (GNC) Conference).