Masters Theses
Abstract
"This work first presents the development of a nearly-optimal control scheme and then evaluates the effectiveness of this nearly-optimal control scheme by comparing it to a non optimal controller in Paper I. It is shown that the proposed nearly-optimal scheme is a significant improvement over the non optimal one in both simulation and hardware tests. The test was carried out on a differentially driven robotic vehicle in both obstacle free and obstacle ridden environments. On the other hand, the controller introduced for the obstacle avoidance in Paper II will also function in the absence of obstacles so that no switching between controllers is required. The tests also show that the nearly optimal control scheme is still well within the capabilities of an embedded processor"--Abstract, page iv.
Advisor(s)
Sarangapani, Jagannathan, 1965-
Committee Member(s)
Acar, Levent
Zawodniok, Maciej Jan, 1975-
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Computer Engineering
Sponsor(s)
National Science Foundation (U.S.)
Publisher
Missouri University of Science and Technology
Publication Date
Fall 2010
Journal article titles appearing in thesis/dissertation
- Optimal control of mobile robot formations in discrete-time using neural networks
- Obstacle avoidance with mobile robot formations using optimal control
Pagination
xi, 93 pages
Note about bibliography
Includes bibliographical references.
Rights
© 2010 Bryan Michael Brenner, All rights reserved.
Document Type
Thesis - Restricted Access
File Type
text
Language
English
Subject Headings
Mobile robots -- Design
Robots -- Control systems -- Design
Thesis Number
T 9721
Print OCLC #
730954219
Electronic OCLC #
911038377
Link to Catalog Record
Electronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.
http://merlin.lib.umsystem.edu/record=b8244129~S5Recommended Citation
Brenner, Bryan M., "Embedded optimal control of mobile robot formations using neural networks" (2010). Masters Theses. 127.
https://scholarsmine.mst.edu/masters_theses/127
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Comments
Financial support provided by the NSF grant ECCS#0621924 in the form of Graduate Research Assistantship