Adaptive Identification of Nonlinear Systems
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Authors
Lehrer, Devon Harold
Date
2010-10-19T18:18:23Z
Type
thesis
Language
eng
Keyword
Nonlinear System Identification , Adaptive Control
Alternative Title
Abstract
This work presents three techniques for parameter identification for nonlinear systems. The methods presented are expanded from those presented in Adetola and Guay [3, 4, 5] and are intended to improve the performance of existing adaptive control systems. The first two methods exactly recover open-loop system parameters once a defined convergence condition is met. In either case, the true parameters are identified when the regressor matrix is of
full rank and can be inverted. The third case uses a novel method developed in Adetola
and Guay [5] to define a parameter uncertainty set. The uncertainty set is periodically updated to shrink around the true value of the parameters. Each method is shown to be applicable to a large class of linearly parameterized nonlinear discrete-time system. In each
case, parameter convergence is guaranteed subject to an appropriate convergence condition, which has been related to a classical persistence of excitation condition. The effectiveness of
the methods is demonstrated using a simulation example. The application of the uncertainty set technique to nonlinearly parameterized systems constitutes the main contribution of the thesis. The parameter uncertainty set method is generalized to the problem of adaptive estimation in nonlinearly parameterized systems, for both continuous-time and discrete-time cases. The method is demonstrated to perform well in simulation for a simplified model of a bioreactor operating under Monod kinetics.
Description
Thesis (Master, Chemical Engineering) -- Queen's University, 2010-10-19 10:58:24.888
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This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.