Doctoral Dissertations


"Traditional nonlinear techniques cannot be directly applicable to control large scale interconnected nonlinear dynamic systems due their sheer size and unavailability of system dynamics. Therefore, in this dissertation, the decentralized adaptive neural network (NN) control of a class of nonlinear interconnected dynamic systems is introduced and its application to power systems is presented in the form of six papers. In the first paper, a new nonlinear dynamical representation in the form of a large scale interconnected system for a power network free of algebraic equations with multiple UPFCs as nonlinear controllers is presented. Then, oscillation damping for UPFCs using adaptive NN control is discussed by assuming that the system dynamics are known. Subsequently, the dynamic surface control (DSC) framework is proposed in continuous-time not only to overcome the need for the subsystem dynamics and interconnection terms, but also to relax the explosion of complexity problem normally observed in traditional backstepping. The application of DSC-based decentralized control of power system with excitation control is shown in the third paper. On the other hand, a novel adaptive NN-based decentralized controller for a class of interconnected discrete-time systems with unknown subsystem and interconnection dynamics is introduced since discrete-time is preferred for implementation. The application of the decentralized controller is shown on a power network. Next, a near optimal decentralized discrete-time controller is introduced in the fifth paper for such systems in affine form whereas the sixth paper proposes a method for obtaining the L2-gain near optimal control while keeping a tradeoff between accuracy and computational complexity. Lyapunov theory is employed to assess the stability of the controllers"--Abstract, page iv.


Sarangapani, Jagannathan, 1965-

Committee Member(s)

Kimball, Jonathan W.
Erickson, Kelvin T.
Dagli, Cihan H., 1949-
Crow, Mariesa


Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering


National Science Foundation (U.S.)
Sandia Laboratories


Missouri University of Science and Technology

Publication Date

Fall 2009

Journal article titles appearing in thesis/dissertation

  • Novel dynamic representation and control of power systems with FACTS devices
  • Decentralized dynamic surface control of large-scale interconnected systems in strict-feedback form using neural networks with asymptotic stabilization
  • Power system stabilization using adaptive neural network-based dynamic surface control
  • Decentralized adaptive neural network control of a class of interconnected nonlinear discrete-time systems with application to power systems
  • Decentralized near optimal control of a class of interconnected nonlinear discrete-time systems by using online Hamilton-Bellman-Jacobi formulation
  • Generalized Hamilton-Jacobi-Isaacs formulation for near optimal control of affine nonlinear discrete-time systems with application to power systems


xvii, 266 pages

Note about bibliography

Includes bibliographical references.


© 2009 Shahab Mehraeen, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Subject Headings

Adaptive control systems
Control theory -- Computer programs
Neural networks (Computer science)
Nonlinear control theory

Thesis Number

T 9560

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