Neural network control for DC motor micromaneuvering
The application of a neural network controller for compensating the effects induced by the friction in a DC motor micromaneuvering system is considered in this article. A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed...
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| Veröffentlicht in: | IEEE transactions on industrial electronics (1982) 1995-10, Vol.42 (5), p.516-523 |
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| container_title | IEEE transactions on industrial electronics (1982) |
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| creator | Tzes, A. Pei-Yuan Peng Chen-Chung Houng |
| description | The application of a neural network controller for compensating the effects induced by the friction in a DC motor micromaneuvering system is considered in this article. A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. The on-line training of the neural network is performed in the region of interest of the output domain. The neural network output resembles that of a pulse width modulated controller. The effect of the number of neurons in the input and hidden layers on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm.< > |
| doi_str_mv | 10.1109/41.464615 |
| format | Article |
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A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. The on-line training of the neural network is performed in the region of interest of the output domain. The neural network output resembles that of a pulse width modulated controller. The effect of the number of neurons in the input and hidden layers on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm.< ></description><identifier>ISSN: 0278-0046</identifier><identifier>EISSN: 1557-9948</identifier><identifier>DOI: 10.1109/41.464615</identifier><identifier>CODEN: ITIED6</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Angular velocity control ; Applied sciences ; Backpropagation algorithms ; Computer science; control theory; systems ; Control systems ; Control theory. 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A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. The on-line training of the neural network is performed in the region of interest of the output domain. The neural network output resembles that of a pulse width modulated controller. The effect of the number of neurons in the input and hidden layers on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm.< ></description><subject>Angular velocity control</subject><subject>Applied sciences</subject><subject>Backpropagation algorithms</subject><subject>Computer science; control theory; systems</subject><subject>Control systems</subject><subject>Control theory. Systems</subject><subject>DC motors</subject><subject>Exact sciences and technology</subject><subject>Friction</subject><subject>History</subject><subject>Neural networks</subject><subject>Pulse width modulation</subject><subject>Robotics</subject><subject>Shafts</subject><subject>Space vector pulse width modulation</subject><issn>0278-0046</issn><issn>1557-9948</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1995</creationdate><recordtype>article</recordtype><recordid>eNqFkD1PwzAQhi0EEqUwsDJlQEgMKXfxR-wRlU-pggVmy3FtFEjiYicg_j2pUrEy3Un33KO7l5BThAUiqCuGCyaYQL5HZsh5mSvF5D6ZQVHKHICJQ3KU0jsAMo58RtiTG6Jpss713yF-ZDZ0fQxN5kPMbpZZG_qxaWsbQ2s6N3y5WHdvx-TAmya5k12dk9e725flQ756vn9cXq9yS2nZ5yiEh4o6UXCqPEdlKo-lB27tGhAMtbBW1vEKcC0VKGG9LFjllaLgSyvonFxM3k0Mn4NLvW7rZF3TjKeEIelCMSqVhP9ByaTiYmu8nMDxoZSi83oT69bEH42gtwFqhnoKcGTPd1KTrGl8NJ2t098CFRLpeMCcnE1Y7Zz7m-4cv1HJdpQ</recordid><startdate>19951001</startdate><enddate>19951001</enddate><creator>Tzes, A.</creator><creator>Pei-Yuan Peng</creator><creator>Chen-Chung Houng</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>7TB</scope><scope>FR3</scope></search><sort><creationdate>19951001</creationdate><title>Neural network control for DC motor micromaneuvering</title><author>Tzes, A. ; Pei-Yuan Peng ; Chen-Chung Houng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-166f0b3e62539f519abf17f05ccd010a3c0d9ce5b01d89096cf824bf9930f7c63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Angular velocity control</topic><topic>Applied sciences</topic><topic>Backpropagation algorithms</topic><topic>Computer science; control theory; systems</topic><topic>Control systems</topic><topic>Control theory. Systems</topic><topic>DC motors</topic><topic>Exact sciences and technology</topic><topic>Friction</topic><topic>History</topic><topic>Neural networks</topic><topic>Pulse width modulation</topic><topic>Robotics</topic><topic>Shafts</topic><topic>Space vector pulse width modulation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tzes, A.</creatorcontrib><creatorcontrib>Pei-Yuan Peng</creatorcontrib><creatorcontrib>Chen-Chung Houng</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on industrial electronics (1982)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tzes, A.</au><au>Pei-Yuan Peng</au><au>Chen-Chung Houng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural network control for DC motor micromaneuvering</atitle><jtitle>IEEE transactions on industrial electronics (1982)</jtitle><stitle>TIE</stitle><date>1995-10-01</date><risdate>1995</risdate><volume>42</volume><issue>5</issue><spage>516</spage><epage>523</epage><pages>516-523</pages><issn>0278-0046</issn><eissn>1557-9948</eissn><coden>ITIED6</coden><abstract>The application of a neural network controller for compensating the effects induced by the friction in a DC motor micromaneuvering system is considered in this article. A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. The on-line training of the neural network is performed in the region of interest of the output domain. The neural network output resembles that of a pulse width modulated controller. The effect of the number of neurons in the input and hidden layers on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm.< ></abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/41.464615</doi><tpages>8</tpages></addata></record> |
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| issn | 0278-0046 1557-9948 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Angular velocity control Applied sciences Backpropagation algorithms Computer science control theory systems Control systems Control theory. Systems DC motors Exact sciences and technology Friction History Neural networks Pulse width modulation Robotics Shafts Space vector pulse width modulation |
| title | Neural network control for DC motor micromaneuvering |
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