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uddissert_djaneye-1__final format approved LW 12-7-17.pdf (3.29 MB)
ETD Abstract Container
Abstract Header
Discrete-time Concurrent Learning for System Identification and Applications: Leveraging Memory Usage for Good Learning
Author Info
Djaneye-Boundjou, Ouboti Seydou Eyanaa
ORCID® Identifier
http://orcid.org/0000-0002-0563-6177
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=dayton151298579862899
Abstract Details
Year and Degree
2017, Doctor of Philosophy (Ph.D.), University of Dayton, Engineering.
Abstract
Literature on system identification reveals that persistently exiting inputs are needed in order to achieve good parameter identification when using standard learning techniques such as Gradient Descent and/or Least Squares for function approximation. However, realizing persistency of excitation in itself is quite demanding, especially in the context of on-line approximation and adaptive control. Much recently, Concurrent Learning (CL), through its utilization of memory (and, in that regard, quite similarly to human learning), has been shown to be able to yield good learning without the need to resort to persistency of excitation. For all intents and purposes, we refer to “good learning” throughout this work as the ability to reconstruct the function(s) being approximated well when using the estimated parameters. The continuous-time (CT) domain literature on CL has seen the larger share of researches. For our part, we have focused on the discrete-time (DT) domain. Tough many systems can be modeled as CT systems, usually, controlling such systems, especially real-time (or, rather close to real-time), is done via the use of digital computers and/or micro-controllers, therefore making DT framework studies compelling. We have shown that, similarly to the CT domain, granted a less restrictive CL condition compared to that of persistency of excitation is verified, analogous CL results to that obtained in the CT domain can also be achieved in the DT domain. Before incorporating and making use of the concept of concurrent learning in our studies, we thoroughly study the Gradient Descent and Least Squares techniques for function approximation and system identification of a dimensionally complex uncertainty, which, to the best our knowledge, is yet to be done in literature. Our main contributions are however the derivations of a DT Normalized Gradient (DTNG) based CL algorithm as well as a DT Normalized Recursive Least Squared (DTNRLS) based CL algorithm for approximation of both DT structured and DT unstructured uncertainties, while showing analytically that our devised algorithms guarantee good parameter identification if the aforesaid CL condition is met. Numerical simulations are provided to show how well the developed CL algorithms leverage memory usage to achieve good learning. The algorithms are also made use of in two applications: the discrete-time indirect adaptive control of a class of discrete-time single state plant bearing parametric or structured uncertainties and the system identification of a robot.
Committee
Raul Ordonez, Ph.D. (Committee Chair)
Keigo Hirakawa, Ph.D. (Committee Member)
Vijayan Asari, Ph.D. (Committee Member)
Paul Eloe, Ph.D. (Committee Member)
Pages
219 p.
Subject Headings
Applied Mathematics
;
Electrical Engineering
;
Engineering
;
Mathematics
Keywords
System identification
;
Function approximation
;
Learning
;
Concurrent Learning
;
Concurrent Learning in the discrete-time domain
;
Discrete-time normalized gradient descent algorithm
;
Discrete-time normalized recursive least squares algorithm
Recommended Citations
Refworks
EndNote
RIS
Mendeley
Citations
Djaneye-Boundjou, O. S. E. (2017).
Discrete-time Concurrent Learning for System Identification and Applications: Leveraging Memory Usage for Good Learning
[Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton151298579862899
APA Style (7th edition)
Djaneye-Boundjou, Ouboti.
Discrete-time Concurrent Learning for System Identification and Applications: Leveraging Memory Usage for Good Learning.
2017. University of Dayton, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=dayton151298579862899.
MLA Style (8th edition)
Djaneye-Boundjou, Ouboti. "Discrete-time Concurrent Learning for System Identification and Applications: Leveraging Memory Usage for Good Learning." Doctoral dissertation, University of Dayton, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton151298579862899
Chicago Manual of Style (17th edition)
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Document number:
dayton151298579862899
Download Count:
522
Copyright Info
© 2017, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.