Nonparametric model learning adaptive control method of DC motor
Nonparametric model learning adaptive control method (NMLAC) presented in this paper is based on new concepts called pseudo-partial-derivatives (PPD) for a class of nonlinear systems. No structural information, no mathematical model, no training process and no external testing signals are needed. Th...
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| creator | Cao Rongmin Zhongsheng Hou Bai Lianping |
| description | Nonparametric model learning adaptive control method (NMLAC) presented in this paper is based on new concepts called pseudo-partial-derivatives (PPD) for a class of nonlinear systems. No structural information, no mathematical model, no training process and no external testing signals are needed. The unmodelled dynamics do not exist. In this paper, nonparametric model learning adaptive control (NMLAC) approach of a class of SISO nonlinear discrete-time systems based on linearization of tight format is applied to DC motor rotate speed control. The design of controller is model-free, based directly on pseudo-partial-derivatives (PPD) derived on-line from the input and output information of the motor motion model using novel parameter estimation algorithms. Simulation experiment examples are provided for real nonlinear systems, which are known to be difficult to model, and control to demonstrate the correctness, effectiveness and advantages of the approaches proposed. |
| doi_str_mv | 10.1109/CCDC.2009.5192352 |
| format | Conference Proceeding |
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No structural information, no mathematical model, no training process and no external testing signals are needed. The unmodelled dynamics do not exist. In this paper, nonparametric model learning adaptive control (NMLAC) approach of a class of SISO nonlinear discrete-time systems based on linearization of tight format is applied to DC motor rotate speed control. The design of controller is model-free, based directly on pseudo-partial-derivatives (PPD) derived on-line from the input and output information of the motor motion model using novel parameter estimation algorithms. 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Simulation experiment examples are provided for real nonlinear systems, which are known to be difficult to model, and control to demonstrate the correctness, effectiveness and advantages of the approaches proposed.</description><subject>Adaptive control</subject><subject>Algorithm design and analysis</subject><subject>computer simulation</subject><subject>DC motor</subject><subject>DC motors</subject><subject>Mathematical model</subject><subject>Motion control</subject><subject>NMLAC</subject><subject>Nonlinear systems</subject><subject>nonlinear systems and stability</subject><subject>Parameter estimation</subject><subject>Signal processing</subject><subject>Testing</subject><subject>Velocity control</subject><issn>1948-9439</issn><issn>1948-9447</issn><isbn>9781424427222</isbn><isbn>1424427223</isbn><isbn>9781424427239</isbn><isbn>1424427231</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkEtLw0AUhcdHwVrzA8TN_IHUe-eRmbtTUl9QdKPrMk1uNJJmyiQI_nsDFsGzOYvv4yyOEJcIS0Sg67JclUsFQEuLpLRVRyIj59EoY5RTmo7FHMn4nIxxJ_-YUqd_TNNMnE8znqAALM5ENgyfMMVYbTXOxc1z7PchhR2Pqa3kLtbcyY5D6tv-XYY67Mf2i2UV-zHFTk7aR6xlbOSqnOQxpgsxa0I3cHbohXi7v3stH_P1y8NTebvOW3R2zGsbTPDkgtHAhauh3jqqDGvUUzWOGCtdOICqMAV5v_UGLUzIam6QQS_E1e9uy8ybfWp3IX1vDufoHyTFUAM</recordid><startdate>200906</startdate><enddate>200906</enddate><creator>Cao Rongmin</creator><creator>Zhongsheng Hou</creator><creator>Bai Lianping</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200906</creationdate><title>Nonparametric model learning adaptive control method of DC motor</title><author>Cao Rongmin ; Zhongsheng Hou ; Bai Lianping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-d5a4a897a430e67d0db79c4e3139c4f79e1c36700c646988b841509c453ef1e03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adaptive control</topic><topic>Algorithm design and analysis</topic><topic>computer simulation</topic><topic>DC motor</topic><topic>DC motors</topic><topic>Mathematical model</topic><topic>Motion control</topic><topic>NMLAC</topic><topic>Nonlinear systems</topic><topic>nonlinear systems and stability</topic><topic>Parameter estimation</topic><topic>Signal processing</topic><topic>Testing</topic><topic>Velocity control</topic><toplevel>online_resources</toplevel><creatorcontrib>Cao Rongmin</creatorcontrib><creatorcontrib>Zhongsheng Hou</creatorcontrib><creatorcontrib>Bai Lianping</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Cao Rongmin</au><au>Zhongsheng Hou</au><au>Bai Lianping</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Nonparametric model learning adaptive control method of DC motor</atitle><btitle>2009 Chinese Control and Decision Conference</btitle><stitle>CCDC</stitle><date>2009-06</date><risdate>2009</risdate><spage>1779</spage><epage>1782</epage><pages>1779-1782</pages><issn>1948-9439</issn><eissn>1948-9447</eissn><isbn>9781424427222</isbn><isbn>1424427223</isbn><eisbn>9781424427239</eisbn><eisbn>1424427231</eisbn><abstract>Nonparametric model learning adaptive control method (NMLAC) presented in this paper is based on new concepts called pseudo-partial-derivatives (PPD) for a class of nonlinear systems. No structural information, no mathematical model, no training process and no external testing signals are needed. The unmodelled dynamics do not exist. In this paper, nonparametric model learning adaptive control (NMLAC) approach of a class of SISO nonlinear discrete-time systems based on linearization of tight format is applied to DC motor rotate speed control. The design of controller is model-free, based directly on pseudo-partial-derivatives (PPD) derived on-line from the input and output information of the motor motion model using novel parameter estimation algorithms. Simulation experiment examples are provided for real nonlinear systems, which are known to be difficult to model, and control to demonstrate the correctness, effectiveness and advantages of the approaches proposed.</abstract><pub>IEEE</pub><doi>10.1109/CCDC.2009.5192352</doi><tpages>4</tpages></addata></record> |
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| issn | 1948-9439 1948-9447 |
| language | eng |
| recordid | cdi_ieee_primary_5192352 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptive control Algorithm design and analysis computer simulation DC motor DC motors Mathematical model Motion control NMLAC Nonlinear systems nonlinear systems and stability Parameter estimation Signal processing Testing Velocity control |
| title | Nonparametric model learning adaptive control method of DC motor |
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