Doctoral Dissertations

Keywords and Phrases

Adaptive Dynamic Programming; Fuzzy Neural Network; Gradient Temporal Difference; Mobile Robot; Model Order Reduction; Reinforcement Learning

Abstract

"This dissertation investigates the application of a variety of computational intelligence techniques, particularly clustering and adaptive dynamic programming (ADP) designs especially heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Moreover, a one-step temporal-difference (TD(0)) and n-step TD (TD(λ)) with their gradients are utilized as learning algorithms to train and online-adapt the families of ADP. The dissertation is organized into seven papers. The first paper demonstrates the robustness of model order reduction (MOR) for simulating complex dynamical systems. Agglomerative hierarchical clustering based on performance evaluation is introduced for MOR. This method computes the reduced order denominator of the transfer function by clustering system poles in a hierarchical dendrogram. Several numerical examples of reducing techniques are taken from the literature to compare with our work. In the second paper, a HDP is combined with the Dyna algorithm for path planning. The third paper uses DHP with an eligibility trace parameter (λ) to track a reference trajectory under uncertainties for a nonholonomic mobile robot by using a first-order Sugeno fuzzy neural network structure for the critic and actor networks. In the fourth and fifth papers, a stability analysis for a model-free action-dependent HDP(λ) is demonstrated with batch- and online-implementation learning, respectively. The sixth work combines two different gradient prediction levels of critic networks. In this work, we provide a convergence proofs. The seventh paper develops a two-hybrid recurrent fuzzy neural network structures for both critic and actor networks. They use a novel n-step gradient temporal-difference (gradient of TD(λ)) of an advanced ADP algorithm called value-gradient learning (VGL(λ)), and convergence proofs are given. Furthermore, the seventh paper is the first to combine the single network adaptive critic with VGL(λ)."--Abstract, page iv.

Advisor(s)

Wunsch, Donald C.

Committee Member(s)

Sarangapani, Jagannathan, 1965-
Stanley, R. Joe
Zawodniok, Maciej Jan, 1975-
Dagli, Cihan H., 1949-

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Sponsor(s)

Missouri University of Science and Technology Intelligent Systems Center
Mary K Finley Missouri Endowment

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2018

Journal article titles appearing in thesis/dissertation

  • Model order reduction based on agglomerative hierarchical clustering
  • Heuristic dynamic programming for mobile robot path planning based on Dyna approach
  • Mobile robot control based on hybrid neuro-fuzzy value gradient reinforcement learning
  • The boundedness conditions for model-free HDP(λ)
  • Online model-free n-step HDP with stability analysis
  • An improved n-step value gradient learning adaptive dynamic programming algorithm for online learning, with convergence proof and case studies
  • Convergence analysis proofs for recurrent neuro-fuzzy value-gradient learning with and without actor

Pagination

xxxi, 341 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2018 Seaar Jewad Kadhim Al-Dabooni, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11253

Electronic OCLC #

1041856869

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