Experiment Research on Complex Optimization Algorithm-Based Adaptive Iterative Learning Control for Electro-Hydraulic Shaking Tables
The adaptive iterative learning control method for electro-hydraulic shaking tables based on the complex optimization algorithm was proposed to overcome the potential stability problem of the traditional iteration control method. The system identification precision’s influence on convergence was ana...
Gespeichert in:
| Veröffentlicht in: | Electronics (Basel) 2023-04, Vol.12 (8), p.1797 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 8 |
| container_start_page | 1797 |
| container_title | Electronics (Basel) |
| container_volume | 12 |
| creator | Zhang, Lianpeng Feng, Jie Hao, Rujiang Hu, Po Liang, Xiao |
| description | The adaptive iterative learning control method for electro-hydraulic shaking tables based on the complex optimization algorithm was proposed to overcome the potential stability problem of the traditional iteration control method. The system identification precision’s influence on convergence was analyzed. Based on the real optimization theory and the mapping relationship between real vector space and complex vector space, the complex Broyden optimization iterative algorithm was proposed, and its stability and convergence was analyzed. To improve the stability and accelerate the convergence of the proposed algorithm, the complex steepest descent algorithm was proposed to cooperate with the complex Broyden optimization algorithm, which can adaptively optimize the complex steepest gradient iterative gain and update the system impedance in real time during the control process. The shaking tables experiment system was designed, applying xPC target rapid prototype control technology, and a series of experimental tests were performed. The results indicated that the proposed control method can quickly and stably converge to the optimal solution no matter whether the system identification error is small or large, and, thus, verified that validity and feasibility of the proposed adaptive iterative learning method. |
| doi_str_mv | 10.3390/electronics12081797 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2806532825</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A747443677</galeid><sourcerecordid>A747443677</sourcerecordid><originalsourceid>FETCH-LOGICAL-c361t-f2006955443167579452ca4b68dac6c56483371930cff6b7514c52c25aa461ea3</originalsourceid><addsrcrecordid>eNptUU1PHDEMHSEqFVF-QS-ROA_kY5JMjstqW5BWQmrpeeTNeHYDM8k0ySLgzA8nsBx6qH2wZT8_P9lV9Z3RCyEMvcQRbY7BO5sYpy3TRh9VJ5xqUxtu-PE_-dfqLKV7Wsww0Qp6Ur2unmaMbkKfyS9MCNHuSPBkGaZ5xCdyO2c3uRfIrhQX4zZEl3dTfQUJe7LoobQfkdxkjPCRrQuDd35bCHwRNZIhRLI6KKyvn_sI-9FZ8nsHD--oO9iMmL5VXwYYE559xtPqz4_V3fK6Xt_-vFku1rUViuV64JQqI2XTCKa01KaR3EKzUW0PVlmpmlYIzYygdhjURkvW2ILgEqBRDEGcVucH3jmGv3tMubsP--jLyo63VEnBWy4L6uKA2sKInfNDyBFs8R4nZ4PHwZX6Qje6CFFalwFxGLAxpBRx6OZyUYjPHaPd-4u6_7xIvAG2xohX</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2806532825</pqid></control><display><type>article</type><title>Experiment Research on Complex Optimization Algorithm-Based Adaptive Iterative Learning Control for Electro-Hydraulic Shaking Tables</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Zhang, Lianpeng ; Feng, Jie ; Hao, Rujiang ; Hu, Po ; Liang, Xiao</creator><creatorcontrib>Zhang, Lianpeng ; Feng, Jie ; Hao, Rujiang ; Hu, Po ; Liang, Xiao</creatorcontrib><description>The adaptive iterative learning control method for electro-hydraulic shaking tables based on the complex optimization algorithm was proposed to overcome the potential stability problem of the traditional iteration control method. The system identification precision’s influence on convergence was analyzed. Based on the real optimization theory and the mapping relationship between real vector space and complex vector space, the complex Broyden optimization iterative algorithm was proposed, and its stability and convergence was analyzed. To improve the stability and accelerate the convergence of the proposed algorithm, the complex steepest descent algorithm was proposed to cooperate with the complex Broyden optimization algorithm, which can adaptively optimize the complex steepest gradient iterative gain and update the system impedance in real time during the control process. The shaking tables experiment system was designed, applying xPC target rapid prototype control technology, and a series of experimental tests were performed. The results indicated that the proposed control method can quickly and stably converge to the optimal solution no matter whether the system identification error is small or large, and, thus, verified that validity and feasibility of the proposed adaptive iterative learning method.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12081797</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Adaptive algorithms ; Adaptive control ; Algorithms ; China ; Control algorithms ; Control methods ; Control systems ; Controllers ; Convergence ; Earthquake resistant design ; Earthquakes ; Experiments ; Hydraulics ; Iterative algorithms ; Machine learning ; Mathematical optimization ; Methods ; Optimization ; Optimization algorithms ; Rapid prototyping ; Shake tables ; Signal processing ; Stability analysis ; System identification ; Systems stability ; Vector spaces</subject><ispartof>Electronics (Basel), 2023-04, Vol.12 (8), p.1797</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c361t-f2006955443167579452ca4b68dac6c56483371930cff6b7514c52c25aa461ea3</citedby><cites>FETCH-LOGICAL-c361t-f2006955443167579452ca4b68dac6c56483371930cff6b7514c52c25aa461ea3</cites><orcidid>0000-0001-8612-1521</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Zhang, Lianpeng</creatorcontrib><creatorcontrib>Feng, Jie</creatorcontrib><creatorcontrib>Hao, Rujiang</creatorcontrib><creatorcontrib>Hu, Po</creatorcontrib><creatorcontrib>Liang, Xiao</creatorcontrib><title>Experiment Research on Complex Optimization Algorithm-Based Adaptive Iterative Learning Control for Electro-Hydraulic Shaking Tables</title><title>Electronics (Basel)</title><description>The adaptive iterative learning control method for electro-hydraulic shaking tables based on the complex optimization algorithm was proposed to overcome the potential stability problem of the traditional iteration control method. The system identification precision’s influence on convergence was analyzed. Based on the real optimization theory and the mapping relationship between real vector space and complex vector space, the complex Broyden optimization iterative algorithm was proposed, and its stability and convergence was analyzed. To improve the stability and accelerate the convergence of the proposed algorithm, the complex steepest descent algorithm was proposed to cooperate with the complex Broyden optimization algorithm, which can adaptively optimize the complex steepest gradient iterative gain and update the system impedance in real time during the control process. The shaking tables experiment system was designed, applying xPC target rapid prototype control technology, and a series of experimental tests were performed. The results indicated that the proposed control method can quickly and stably converge to the optimal solution no matter whether the system identification error is small or large, and, thus, verified that validity and feasibility of the proposed adaptive iterative learning method.</description><subject>Adaptive algorithms</subject><subject>Adaptive control</subject><subject>Algorithms</subject><subject>China</subject><subject>Control algorithms</subject><subject>Control methods</subject><subject>Control systems</subject><subject>Controllers</subject><subject>Convergence</subject><subject>Earthquake resistant design</subject><subject>Earthquakes</subject><subject>Experiments</subject><subject>Hydraulics</subject><subject>Iterative algorithms</subject><subject>Machine learning</subject><subject>Mathematical optimization</subject><subject>Methods</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Rapid prototyping</subject><subject>Shake tables</subject><subject>Signal processing</subject><subject>Stability analysis</subject><subject>System identification</subject><subject>Systems stability</subject><subject>Vector spaces</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUU1PHDEMHSEqFVF-QS-ROA_kY5JMjstqW5BWQmrpeeTNeHYDM8k0ySLgzA8nsBx6qH2wZT8_P9lV9Z3RCyEMvcQRbY7BO5sYpy3TRh9VJ5xqUxtu-PE_-dfqLKV7Wsww0Qp6Ur2unmaMbkKfyS9MCNHuSPBkGaZ5xCdyO2c3uRfIrhQX4zZEl3dTfQUJe7LoobQfkdxkjPCRrQuDd35bCHwRNZIhRLI6KKyvn_sI-9FZ8nsHD--oO9iMmL5VXwYYE559xtPqz4_V3fK6Xt_-vFku1rUViuV64JQqI2XTCKa01KaR3EKzUW0PVlmpmlYIzYygdhjURkvW2ILgEqBRDEGcVucH3jmGv3tMubsP--jLyo63VEnBWy4L6uKA2sKInfNDyBFs8R4nZ4PHwZX6Qje6CFFalwFxGLAxpBRx6OZyUYjPHaPd-4u6_7xIvAG2xohX</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Zhang, Lianpeng</creator><creator>Feng, Jie</creator><creator>Hao, Rujiang</creator><creator>Hu, Po</creator><creator>Liang, Xiao</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0001-8612-1521</orcidid></search><sort><creationdate>20230401</creationdate><title>Experiment Research on Complex Optimization Algorithm-Based Adaptive Iterative Learning Control for Electro-Hydraulic Shaking Tables</title><author>Zhang, Lianpeng ; Feng, Jie ; Hao, Rujiang ; Hu, Po ; Liang, Xiao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c361t-f2006955443167579452ca4b68dac6c56483371930cff6b7514c52c25aa461ea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adaptive algorithms</topic><topic>Adaptive control</topic><topic>Algorithms</topic><topic>China</topic><topic>Control algorithms</topic><topic>Control methods</topic><topic>Control systems</topic><topic>Controllers</topic><topic>Convergence</topic><topic>Earthquake resistant design</topic><topic>Earthquakes</topic><topic>Experiments</topic><topic>Hydraulics</topic><topic>Iterative algorithms</topic><topic>Machine learning</topic><topic>Mathematical optimization</topic><topic>Methods</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Rapid prototyping</topic><topic>Shake tables</topic><topic>Signal processing</topic><topic>Stability analysis</topic><topic>System identification</topic><topic>Systems stability</topic><topic>Vector spaces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Lianpeng</creatorcontrib><creatorcontrib>Feng, Jie</creatorcontrib><creatorcontrib>Hao, Rujiang</creatorcontrib><creatorcontrib>Hu, Po</creatorcontrib><creatorcontrib>Liang, Xiao</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</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><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Lianpeng</au><au>Feng, Jie</au><au>Hao, Rujiang</au><au>Hu, Po</au><au>Liang, Xiao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Experiment Research on Complex Optimization Algorithm-Based Adaptive Iterative Learning Control for Electro-Hydraulic Shaking Tables</atitle><jtitle>Electronics (Basel)</jtitle><date>2023-04-01</date><risdate>2023</risdate><volume>12</volume><issue>8</issue><spage>1797</spage><pages>1797-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>The adaptive iterative learning control method for electro-hydraulic shaking tables based on the complex optimization algorithm was proposed to overcome the potential stability problem of the traditional iteration control method. The system identification precision’s influence on convergence was analyzed. Based on the real optimization theory and the mapping relationship between real vector space and complex vector space, the complex Broyden optimization iterative algorithm was proposed, and its stability and convergence was analyzed. To improve the stability and accelerate the convergence of the proposed algorithm, the complex steepest descent algorithm was proposed to cooperate with the complex Broyden optimization algorithm, which can adaptively optimize the complex steepest gradient iterative gain and update the system impedance in real time during the control process. The shaking tables experiment system was designed, applying xPC target rapid prototype control technology, and a series of experimental tests were performed. The results indicated that the proposed control method can quickly and stably converge to the optimal solution no matter whether the system identification error is small or large, and, thus, verified that validity and feasibility of the proposed adaptive iterative learning method.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics12081797</doi><orcidid>https://orcid.org/0000-0001-8612-1521</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2079-9292 |
| ispartof | Electronics (Basel), 2023-04, Vol.12 (8), p.1797 |
| issn | 2079-9292 2079-9292 |
| language | eng |
| recordid | cdi_proquest_journals_2806532825 |
| source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Adaptive algorithms Adaptive control Algorithms China Control algorithms Control methods Control systems Controllers Convergence Earthquake resistant design Earthquakes Experiments Hydraulics Iterative algorithms Machine learning Mathematical optimization Methods Optimization Optimization algorithms Rapid prototyping Shake tables Signal processing Stability analysis System identification Systems stability Vector spaces |
| title | Experiment Research on Complex Optimization Algorithm-Based Adaptive Iterative Learning Control for Electro-Hydraulic Shaking Tables |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T17%3A27%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Experiment%20Research%20on%20Complex%20Optimization%20Algorithm-Based%20Adaptive%20Iterative%20Learning%20Control%20for%20Electro-Hydraulic%20Shaking%20Tables&rft.jtitle=Electronics%20(Basel)&rft.au=Zhang,%20Lianpeng&rft.date=2023-04-01&rft.volume=12&rft.issue=8&rft.spage=1797&rft.pages=1797-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics12081797&rft_dat=%3Cgale_proqu%3EA747443677%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2806532825&rft_id=info:pmid/&rft_galeid=A747443677&rfr_iscdi=true |