Advanced neural network controllers and classifiers based on sliding mode training algorithms

Nguyen, VMT

Advanced neural network controllers and classifiers based on sliding mode training algorithms

Nguyen, VMT

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Publication Type:: Thesis
Issue Date:: 2006

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Field	Value	Language
dc.contributor.author	Nguyen, VMT
dc.date.accessioned	2010-05-05T04:56:43Z
dc.date.accessioned	2012-12-15T03:53:02Z
dc.date.available	2010-05-05T04:56:43Z
dc.date.available	2012-12-15T03:53:02Z
dc.date.issued	2006
dc.identifier.uri	http://hdl.handle.net/10453/20256
dc.description	University of Technology, Sydney. Faculty of Engineering.	en
dc.description.abstract	This thesis presents the research undertaken to develop some novel learning algorithms based on the sliding-mode control techniques for the neural network classifiers and controllers. Although the feedforward neural network with the backpropagation learning algorithm is the most widely used approach for classification and control applications, the slow convergence rate. the local minima problem, the difficulties in system identification and the lack of robustness are the issues existing for these neural networkbased systems. The combination of the sliding-mode control techniques and the backpropagation algorithm, as described in this thesis, leads to three novel learning algorithms, which offer effective solutions for these problems. The first learning algorithm, derived from the integration between the chattering-free sliding-mode control technique and the backpropagation algorithm, can obtain fast and global convergence with less computation. Experiment results relating to the headmovement neural classifier for wheelchair control show that the proposed approach considerably improved the convergence speed, global convergence capability and even the generalisation performance of the neural network classifier, in comparison with various popular learning algorithms. The second learning algorithm, also derived from the integration between the chattering-free sliding-mode control technique and the backpropagation algorithm, can guarantee the stability and robustness of the neural control system with parameter uncertainties. Based on this stable neural controller, a neural control design methodology is developed for a class of uncertain nonlinear systems with transportation lag, wherein a new training procedure is proposed to avoid the difficult choice of the training inputs always associated with the conventional neural network identifier. The implementation results with a real-time Static VAR Compensator system indicate the effectiveness of the proposed method. The third on-line learning algorithm, developed from the reaching law method combined with the backpropagation algorithm, offers a robust adaptation approach for the neural control systems with parameter uncertainties and disturbances. The neural control approach is further developed to design a novel decentralised neural controller for a class of uncertain large-scale systems with bounds of interconnections and disturbances. The stability and robustness of the neural control system are guaranteed based on the Lyapunov synthesis. Real-time implementation results for a Coupled Electric Drives CE8 system show the effectiveness and feasibility of the proposed approach.	en
dc.format	Thesis (PhD)
dc.language.iso	en	en
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/20256/10/02whole.pdf
dc.relation.replaces	http://hdl.handle.net/2100/1058
dc.rights	au.edu.uts.lib/ppc
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Advanced neural network controllers and classifiers based on sliding mode training algorithms	en
dc.type	Thesis
utslib.copyright.status	open_access

Abstract:

This thesis presents the research undertaken to develop some novel learning algorithms based on the sliding-mode control techniques for the neural network classifiers and controllers. Although the feedforward neural network with the backpropagation learning algorithm is the most widely used approach for classification and control applications, the slow convergence rate. the local minima problem, the difficulties in system identification and the lack of robustness are the issues existing for these neural networkbased systems. The combination of the sliding-mode control techniques and the backpropagation algorithm, as described in this thesis, leads to three novel learning algorithms, which offer effective solutions for these problems. The first learning algorithm, derived from the integration between the chattering-free sliding-mode control technique and the backpropagation algorithm, can obtain fast and global convergence with less computation. Experiment results relating to the headmovement neural classifier for wheelchair control show that the proposed approach considerably improved the convergence speed, global convergence capability and even the generalisation performance of the neural network classifier, in comparison with various popular learning algorithms. The second learning algorithm, also derived from the integration between the chattering-free sliding-mode control technique and the backpropagation algorithm, can guarantee the stability and robustness of the neural control system with parameter uncertainties. Based on this stable neural controller, a neural control design methodology is developed for a class of uncertain nonlinear systems with transportation lag, wherein a new training procedure is proposed to avoid the difficult choice of the training inputs always associated with the conventional neural network identifier. The implementation results with a real-time Static VAR Compensator system indicate the effectiveness of the proposed method. The third on-line learning algorithm, developed from the reaching law method combined with the backpropagation algorithm, offers a robust adaptation approach for the neural control systems with parameter uncertainties and disturbances. The neural control approach is further developed to design a novel decentralised neural controller for a class of uncertain large-scale systems with bounds of interconnections and disturbances. The stability and robustness of the neural control system are guaranteed based on the Lyapunov synthesis. Real-time implementation results for a Coupled Electric Drives CE8 system show the effectiveness and feasibility of the proposed approach.

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