University of Illinois Urbana-Champaign

Flight evaluation of deep model reference adaptive control

Virdi, Jasvir

Content Files
VIRDI-THESIS-2020.pdf

Permalink

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
Date of Ingest
2020-08-26T21:57:58Z
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.
Graduation Semester
2020-05
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/108017
Copyright and License Information
Copyright 2020 Jasvir Virdi

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Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Mechanical Science and Engineering