Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

  • Bal K. Thapa

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.
Date of Award2001
Original languageEnglish
SupervisorBarry Jones (Supervisor) & Quanlei Zhu (Supervisor)

Keywords

  • Neural network
  • self tuning
  • adaptive control application
  • non-linear control
  • dynamic systems

Cite this

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems
Thapa, B. K. (Author). 2001

Student thesis: Doctoral ThesisDoctor of Philosophy