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Path Following Using A Learning Neural Network

NHH , Mohamad Hanif (2004) Path Following Using A Learning Neural Network. Universiti Teknologi Petronas. (Unpublished)

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This thesis is a development ofprevious works done by [2} on capability of neural controller to efficiently track prescribed paths. Equipped with knowledge on optimal preview control obtained from [1], the initial weights of linear and nonlinear neural controller are initialized to the optimal gains. The implemented neural controller will in turn minimize a performance index, which includes the lateral and attitude angle errors ofvehicle models with respect to the paths. The thesis differs from [2] in a sense that different types of neural controller are established to achieve a better path following accuracy. Two algorithms, gradient descent and quasi-Newton which utilize a batch training method, are introduced as comparison to the gradient descent method that incorporates the online (or incremental) training method. The class of learning (whether good or bad) of the neural controllers is evaluated from the obtained percentage of average weight change, maximum path and yaw attitude angle errors as well as the maximum steering wheel angle. The behaviors oflearning rates and updated weights are given special attention in this thesis. To conduct the specified works, the MATLAB programs written by [2] have been extended and modified.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Embedded Systems
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 30 Sep 2013 16:55
Last Modified: 25 Jan 2017 09:46
URI: http://utpedia.utp.edu.my/id/eprint/7545

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