High Performance of Nonlinear DC Motor Speed Control using Backpropagation Neural Network

Saidah, Saidah (2010) High Performance of Nonlinear DC Motor Speed Control using Backpropagation Neural Network. In: PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON GREEN COMPUTING AND THE SECOND AUN/SEED-NET REGIONAL CONFERENCE ON ICT, 2 Maret 2010, Yogyakarta.

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Conventional PID of controllers for nonlinier DC motors have poor performance when changes of the motor or load dynamics take place. To make improve the performance, an adaptive neural speed controller of a nonlinier dc motor is proposed. BackPropagation neural network (BPNN) is used to appoximate the unknown dynamics. BPNN is trained by the online backpropagation algorithm. The output of the BPNN gives the control voltage applied to the dc motor. The difference between the reference and the actual rotor speed of the nonlinier motor is backpropagated through the BPNN at each step of the control process for updating the connection weights of the BPNN. The control scheme requires neither a knowledge of any motor parameters, nor preferential training of the BPNN. The performance of the controller is simulated and then it is compared with conventional controller or PID in fluctuation disturbance.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: component; Nonlinier DC Motor; BPNN;
Subjects: Technology > Civil Engineering
Technology > Civil Engineering
Divisions: Faculty of Engineering > Bachelor of Electrical Engineering
Depositing User: Perpus Ubhara Surabaya
Date Deposited: 17 Oct 2022 06:08
Last Modified: 11 Sep 2023 02:53
URI: http://eprints.ubhara.ac.id/id/eprint/1443

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