Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13751
Title: DC motor position and speed tracking (PAST) system using neural networks
Authors: KISHORE DIGAMBER RANE
Keywords: Adaptive control, Artificial Neural Network, position control, DC motor, speed control, Intelligent control
Issue Date: 9-Feb-2004
Source: KISHORE DIGAMBER RANE (2004-02-09). DC motor position and speed tracking (PAST) system using neural networks. ScholarBank@NUS Repository.
Abstract: The aim of the thesis is to design and implement an artificial neural network based speed and position control system for a DC motor. The PAST system can drive the DC motor to achieve any arbitrary desired trajectory as well as the speed control. Such tracking is of importance in robotic manipulators. The goal is to achieve accurate control over trajectory especially when unknown load parameters are used. The inverse model of the motor with unknown characteristics is identified through a multi-layer perceptron neural network. The trained neural network is integrated with the concepts of model reference adaptive controller to achieve speed trajectory control. To enhance the capability of the controller to achieve accurate position control, an intuitive feedback module is designed which amplifies the position error through a feedback gain and modifies the speed inputs to the inverse model to achieve accurate position control.
URI: http://scholarbank.nus.edu.sg/handle/10635/13751
Appears in Collections:Master's Theses (Open)

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