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Nonlinear system identification and control using dynamic multi-time scales neural networks

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Nonlinear system identification and control using dynamic multi-time scales neural networks

Han, Xuan (2010) Nonlinear system identification and control using dynamic multi-time scales neural networks. Masters thesis, Concordia University.

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Abstract

In this thesis, on-line identification algorithm and adaptive control design are proposed for nonlinear singularly perturbed systems which are represented by dynamic neural network model with multi-time scales. A novel on-line identification law for the Neural Network weights and linear part matrices of the model has been developed to minimize the identification errors. Based on the identification results, an adaptive controller is developed to achieve trajectory tracking. The Lyapunov synthesis method is used to conduct stability analysis for both identification algorithm and control design. To further enhance the stability and performance of the control system, an improved . dynamic neural network model is proposed by replacing all the output signals from the plant with the state variables of the neural network. Accordingly, the updating laws are modified with a dead-zone function to prevent parameter drifting. By combining feedback linearization with one of three classical control methods such as direct compensator, sliding mode controller or energy function compensation scheme, three different adaptive controllers have been proposed for trajectory tracking. New Lyapunov function analysis method is applied for the stability analysis of the improved identification algorithm and three control systems. Extensive simulation results are provided to support the effectiveness of the proposed identification algorithms and control systems for both dynamic NN models.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical and Industrial Engineering
Item Type:Thesis (Masters)
Authors:Han, Xuan
Pagination:xii, 111 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical and Industrial Engineering
Date:2010
Thesis Supervisor(s):Xie, W. F
ID Code:979222
Deposited By: Concordia University Library
Deposited On:09 Dec 2014 17:55
Last Modified:18 Jan 2018 17:48
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