Flight evaluation of deep model reference adaptive control

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Virdi, Jasvir

Contributors dc:contributor

• Chowdhary, Girish

• Safety Critical Systems
• Disturbance rejection
• Adaptive Control
• Machine Learning

Statement dc:rights

• Copyright 2020 Jasvir Virdi

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108017

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108017