Flight evaluation of deep model reference adaptive control
Virdi, Jasvir
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https://hdl.handle.net/2142/108017
Description
Title
Flight evaluation of deep model reference adaptive control
Author(s)
Virdi, Jasvir
Issue Date
2020-05-12
Director of Research (if dissertation) or Advisor (if thesis)
Chowdhary, Girish
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Adaptive Control
Machine Learning
Safety Critical Systems
Disturbance rejection
Abstract
This thesis presents flight test results for a new neuroadaptive architecture: Deep Neural Network based Model Reference Adaptive Control (DMRAC). This architecture utilizes the power of deep neural network representations for modeling significant nonlinearities while marrying it with the boundedness guarantees that characterize MRAC based controllers. Through experiments on a real quadcopter platform, it is shown that DMRAC can outperform state of the art controllers in different flight regimes while having long-term learning abilities. This makes DMRAC a highly powerful architecture for high-performance control of nonlinear systems.
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