Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications
This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for ae...
Gespeichert in:
| Veröffentlicht in: | Journal of sensors 2019-01, Vol.2019 (2019), p.1-15 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 15 |
|---|---|
| container_issue | 2019 |
| container_start_page | 1 |
| container_title | Journal of sensors |
| container_volume | 2019 |
| creator | Zhao, Libo Li, Lingling Jia, Yuan Gao, Hao Zhou, X. Yu, Ruifang |
| description | This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously. |
| doi_str_mv | 10.1155/2019/5067081 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2204526803</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2204526803</sourcerecordid><originalsourceid>FETCH-LOGICAL-c427t-39bd4f895cd4b1cfea1ea4f3c3af3531011e70da4e89b2a485ee5adcb8db9b9d3</originalsourceid><addsrcrecordid>eNqF0E1Lw0AQBuAgCn7ePMuCR43dzWab5Fiq1UKpxQ_wFibZia4k2bi7Rdvf4I92a0SPwsDM4WGGeYPgmNELxoQYRJRlA0GHCU3ZVrDHhmkSJtEw3f6dxdNusG_tK6VDnnC-F3wuwECDDg257Zxq1Bqc0i3xNZnPB4vpJRnrptPLVvqhdUbXtbeVNgTIw4tBDEcfypJpi8YpqOsVuXdQqFqtUZJFDc7T5tuP0HhA7rDRDsk9tla1z2TUdbUqv4_aw2Cngtri0U8_CB4nVw_jm3B2ez0dj2ZhGUeJC3lWyLhKM1HKuGBlhcAQ4oqXHCouOKOMYUIlxJhmRQRxKhAFyLJIZZEVmeQHwWm_tzP6bYnW5a96aVp_Mo8iGgsfGeVenfeqNNpag1XeGdWAWeWM5pu8803e-U_enp_1_EW1Et7Vf_qk1-gNVvCnWUYz_8UXbAKMTA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2204526803</pqid></control><display><type>article</type><title>Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Wiley Online Library Open Access</source><source>Alma/SFX Local Collection</source><creator>Zhao, Libo ; Li, Lingling ; Jia, Yuan ; Gao, Hao ; Zhou, X. ; Yu, Ruifang</creator><contributor>Imperatore, Pasquale ; Pasquale Imperatore</contributor><creatorcontrib>Zhao, Libo ; Li, Lingling ; Jia, Yuan ; Gao, Hao ; Zhou, X. ; Yu, Ruifang ; Imperatore, Pasquale ; Pasquale Imperatore</creatorcontrib><description>This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.</description><identifier>ISSN: 1687-725X</identifier><identifier>EISSN: 1687-7268</identifier><identifier>DOI: 10.1155/2019/5067081</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Adaptive control ; Agriculture ; Aircraft ; Artificial neural networks ; Back propagation ; Back propagation networks ; Computer simulation ; Control algorithms ; Control methods ; Controllers ; Convergence ; Fuzzy control ; Fuzzy logic ; Kalman filters ; Neural networks ; Optimization algorithms ; Optimization techniques ; Parameters ; Particle swarm optimization ; Proportional integral derivative ; Remote sensing ; Remote sensing systems ; Robust control ; Sensors ; Stabilized platforms ; Three axis stabilization</subject><ispartof>Journal of sensors, 2019-01, Vol.2019 (2019), p.1-15</ispartof><rights>Copyright © 2019 Xiangyang Zhou et al.</rights><rights>Copyright © 2019 Xiangyang Zhou et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c427t-39bd4f895cd4b1cfea1ea4f3c3af3531011e70da4e89b2a485ee5adcb8db9b9d3</citedby><cites>FETCH-LOGICAL-c427t-39bd4f895cd4b1cfea1ea4f3c3af3531011e70da4e89b2a485ee5adcb8db9b9d3</cites><orcidid>0000-0001-6101-8173 ; 0000-0002-3439-7371 ; 0000-0001-9836-271X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><contributor>Imperatore, Pasquale</contributor><contributor>Pasquale Imperatore</contributor><creatorcontrib>Zhao, Libo</creatorcontrib><creatorcontrib>Li, Lingling</creatorcontrib><creatorcontrib>Jia, Yuan</creatorcontrib><creatorcontrib>Gao, Hao</creatorcontrib><creatorcontrib>Zhou, X.</creatorcontrib><creatorcontrib>Yu, Ruifang</creatorcontrib><title>Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications</title><title>Journal of sensors</title><description>This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.</description><subject>Accuracy</subject><subject>Adaptive control</subject><subject>Agriculture</subject><subject>Aircraft</subject><subject>Artificial neural networks</subject><subject>Back propagation</subject><subject>Back propagation networks</subject><subject>Computer simulation</subject><subject>Control algorithms</subject><subject>Control methods</subject><subject>Controllers</subject><subject>Convergence</subject><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Kalman filters</subject><subject>Neural networks</subject><subject>Optimization algorithms</subject><subject>Optimization techniques</subject><subject>Parameters</subject><subject>Particle swarm optimization</subject><subject>Proportional integral derivative</subject><subject>Remote sensing</subject><subject>Remote sensing systems</subject><subject>Robust control</subject><subject>Sensors</subject><subject>Stabilized platforms</subject><subject>Three axis stabilization</subject><issn>1687-725X</issn><issn>1687-7268</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0E1Lw0AQBuAgCn7ePMuCR43dzWab5Fiq1UKpxQ_wFibZia4k2bi7Rdvf4I92a0SPwsDM4WGGeYPgmNELxoQYRJRlA0GHCU3ZVrDHhmkSJtEw3f6dxdNusG_tK6VDnnC-F3wuwECDDg257Zxq1Bqc0i3xNZnPB4vpJRnrptPLVvqhdUbXtbeVNgTIw4tBDEcfypJpi8YpqOsVuXdQqFqtUZJFDc7T5tuP0HhA7rDRDsk9tla1z2TUdbUqv4_aw2Cngtri0U8_CB4nVw_jm3B2ez0dj2ZhGUeJC3lWyLhKM1HKuGBlhcAQ4oqXHCouOKOMYUIlxJhmRQRxKhAFyLJIZZEVmeQHwWm_tzP6bYnW5a96aVp_Mo8iGgsfGeVenfeqNNpag1XeGdWAWeWM5pu8803e-U_enp_1_EW1Et7Vf_qk1-gNVvCnWUYz_8UXbAKMTA</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Zhao, Libo</creator><creator>Li, Lingling</creator><creator>Jia, Yuan</creator><creator>Gao, Hao</creator><creator>Zhou, X.</creator><creator>Yu, Ruifang</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SP</scope><scope>7U5</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KB.</scope><scope>L6V</scope><scope>L7M</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-6101-8173</orcidid><orcidid>https://orcid.org/0000-0002-3439-7371</orcidid><orcidid>https://orcid.org/0000-0001-9836-271X</orcidid></search><sort><creationdate>20190101</creationdate><title>Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications</title><author>Zhao, Libo ; Li, Lingling ; Jia, Yuan ; Gao, Hao ; Zhou, X. ; Yu, Ruifang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c427t-39bd4f895cd4b1cfea1ea4f3c3af3531011e70da4e89b2a485ee5adcb8db9b9d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accuracy</topic><topic>Adaptive control</topic><topic>Agriculture</topic><topic>Aircraft</topic><topic>Artificial neural networks</topic><topic>Back propagation</topic><topic>Back propagation networks</topic><topic>Computer simulation</topic><topic>Control algorithms</topic><topic>Control methods</topic><topic>Controllers</topic><topic>Convergence</topic><topic>Fuzzy control</topic><topic>Fuzzy logic</topic><topic>Kalman filters</topic><topic>Neural networks</topic><topic>Optimization algorithms</topic><topic>Optimization techniques</topic><topic>Parameters</topic><topic>Particle swarm optimization</topic><topic>Proportional integral derivative</topic><topic>Remote sensing</topic><topic>Remote sensing systems</topic><topic>Robust control</topic><topic>Sensors</topic><topic>Stabilized platforms</topic><topic>Three axis stabilization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Libo</creatorcontrib><creatorcontrib>Li, Lingling</creatorcontrib><creatorcontrib>Jia, Yuan</creatorcontrib><creatorcontrib>Gao, Hao</creatorcontrib><creatorcontrib>Zhou, X.</creatorcontrib><creatorcontrib>Yu, Ruifang</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of sensors</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Libo</au><au>Li, Lingling</au><au>Jia, Yuan</au><au>Gao, Hao</au><au>Zhou, X.</au><au>Yu, Ruifang</au><au>Imperatore, Pasquale</au><au>Pasquale Imperatore</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications</atitle><jtitle>Journal of sensors</jtitle><date>2019-01-01</date><risdate>2019</risdate><volume>2019</volume><issue>2019</issue><spage>1</spage><epage>15</epage><pages>1-15</pages><issn>1687-725X</issn><eissn>1687-7268</eissn><abstract>This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2019/5067081</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-6101-8173</orcidid><orcidid>https://orcid.org/0000-0002-3439-7371</orcidid><orcidid>https://orcid.org/0000-0001-9836-271X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1687-725X |
| ispartof | Journal of sensors, 2019-01, Vol.2019 (2019), p.1-15 |
| issn | 1687-725X 1687-7268 |
| language | eng |
| recordid | cdi_proquest_journals_2204526803 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Online Library Open Access; Alma/SFX Local Collection |
| subjects | Accuracy Adaptive control Agriculture Aircraft Artificial neural networks Back propagation Back propagation networks Computer simulation Control algorithms Control methods Controllers Convergence Fuzzy control Fuzzy logic Kalman filters Neural networks Optimization algorithms Optimization techniques Parameters Particle swarm optimization Proportional integral derivative Remote sensing Remote sensing systems Robust control Sensors Stabilized platforms Three axis stabilization |
| title | Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T21%3A48%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Parameter%20Optimization%20on%20FNN/PID%20Compound%20Controller%20for%20a%20Three-Axis%20Inertially%20Stabilized%20Platform%20for%20Aerial%20Remote%20Sensing%20Applications&rft.jtitle=Journal%20of%20sensors&rft.au=Zhao,%20Libo&rft.date=2019-01-01&rft.volume=2019&rft.issue=2019&rft.spage=1&rft.epage=15&rft.pages=1-15&rft.issn=1687-725X&rft.eissn=1687-7268&rft_id=info:doi/10.1155/2019/5067081&rft_dat=%3Cproquest_cross%3E2204526803%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2204526803&rft_id=info:pmid/&rfr_iscdi=true |