Programming-Based Optimal Learning Sliding Mode Control for Cooperative Dual Ship-Mounted Cranes Against Unmatched External Disturbances
When lifting and transporting large payloads in the marine environment, the dual ship-mounted crane system plays a very important role for cargos transportation, which exhibits strong load capacity and high flexibility. However, apart from the nonlinearity and underactuation characteristic, some unk...
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Veröffentlicht in: | IEEE transactions on automation science and engineering 2023-04, Vol.20 (2), p.969-980 |
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description | When lifting and transporting large payloads in the marine environment, the dual ship-mounted crane system plays a very important role for cargos transportation, which exhibits strong load capacity and high flexibility. However, apart from the nonlinearity and underactuation characteristic, some unknown or uncertain unmatched wave disturbances may also cause positioning errors, which may induce various risks during the transportation process; besides, lots of existing methods ignore a part of the cooperative crane motions, and the control issue of dual ship-mounted crane system with five degrees of freedoms (5 DOF) is still open. In terms of the aforementioned problems, an adaptive dynamic programming (ADP)-based optimal learning sliding mode controller is proposed in this paper. Specifically, under the frame of adaptive dynamic programming, the Hamilton-Jacobi-Bellman (HJB) equation can be addressed. Then, based on the gradient attenuation algorithm, critic neural networks (NNs) are trained depending on the designed updating law and the approximate optimal learning controller can be obtained. Lyapunov techniques and Lasalle's invariance principle are used to guarantee asymptotic stability of the dual ship-mounted cranes system. Finally, a series of simulation results are depicted to show the effectiveness of the proposed optimal learning sliding mode controller. Note to Practitioners-In this paper, the control problem of a 5 DOF dual ship-mounted cranes system with unmatched wave disturbance is studied. Due to the complex nonlinear characteristics of the dual ship-mounted cranes and the need to consider the cooperative lifting and wave disturbances, the operation of the dual ship-mounted cranes are very challenging. Moreover, it brings more difficulties to the design of this kind of controller due to its nonlinear underactuated property. Existing control methods for dual ship-mounted cranes are based on linearized or oversimplified crane models, or require accurate model. To solve these kinds of problems, this paper proposes a new control approach for dual ship-mounted cranes suffering from unmatched wave disturbances to achieve satisfactory performance. The stability analysis of the closed-loop systems equilibrium point is implemented by Lyapunov techniques, implying that the accurate positioning and fast payload swings suppression against unmatched wave disturbances are achieved concurrently. In the future studies, we will apply the proposed control met |
doi_str_mv | 10.1109/TASE.2022.3182720 |
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However, apart from the nonlinearity and underactuation characteristic, some unknown or uncertain unmatched wave disturbances may also cause positioning errors, which may induce various risks during the transportation process; besides, lots of existing methods ignore a part of the cooperative crane motions, and the control issue of dual ship-mounted crane system with five degrees of freedoms (5 DOF) is still open. In terms of the aforementioned problems, an adaptive dynamic programming (ADP)-based optimal learning sliding mode controller is proposed in this paper. Specifically, under the frame of adaptive dynamic programming, the Hamilton-Jacobi-Bellman (HJB) equation can be addressed. Then, based on the gradient attenuation algorithm, critic neural networks (NNs) are trained depending on the designed updating law and the approximate optimal learning controller can be obtained. Lyapunov techniques and Lasalle's invariance principle are used to guarantee asymptotic stability of the dual ship-mounted cranes system. Finally, a series of simulation results are depicted to show the effectiveness of the proposed optimal learning sliding mode controller. Note to Practitioners-In this paper, the control problem of a 5 DOF dual ship-mounted cranes system with unmatched wave disturbance is studied. Due to the complex nonlinear characteristics of the dual ship-mounted cranes and the need to consider the cooperative lifting and wave disturbances, the operation of the dual ship-mounted cranes are very challenging. Moreover, it brings more difficulties to the design of this kind of controller due to its nonlinear underactuated property. Existing control methods for dual ship-mounted cranes are based on linearized or oversimplified crane models, or require accurate model. To solve these kinds of problems, this paper proposes a new control approach for dual ship-mounted cranes suffering from unmatched wave disturbances to achieve satisfactory performance. The stability analysis of the closed-loop systems equilibrium point is implemented by Lyapunov techniques, implying that the accurate positioning and fast payload swings suppression against unmatched wave disturbances are achieved concurrently. In the future studies, we will apply the proposed control method to industrial dual ship-mounted cranes systems to improve their working safety and efficiency.</description><identifier>ISSN: 1545-5955</identifier><identifier>EISSN: 1558-3783</identifier><identifier>DOI: 10.1109/TASE.2022.3182720</identifier><identifier>CODEN: ITASC7</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>adaptive dynamic programming ; Algorithms ; Artificial neural networks ; Closed loops ; Control methods ; Controllers ; Cooperative control ; Cranes ; Cranes & hoists ; critic neural network ; Disturbances ; Dual ship-mounted cranes system ; Dynamic programming ; Feedback control ; Learning ; Marine environment ; Mathematical models ; Neural networks ; Nonlinear dynamical systems ; nonlinear system ; Nonlinearity ; Optimal control ; optimal learning sliding mode control ; Payloads ; Robots ; Sliding mode control ; Stability analysis ; Transportation</subject><ispartof>IEEE transactions on automation science and engineering, 2023-04, Vol.20 (2), p.969-980</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-f4a767c0f0ca72fb7b4132f8fa69061920ea3b870788b4642156b7ca05d919473</citedby><cites>FETCH-LOGICAL-c293t-f4a767c0f0ca72fb7b4132f8fa69061920ea3b870788b4642156b7ca05d919473</cites><orcidid>0000-0002-9734-2132 ; 0000-0001-8084-5799 ; 0000-0002-1564-6272 ; 0000-0002-3061-2708</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9797731$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9797731$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Qian, Yuzhe</creatorcontrib><creatorcontrib>Hu, Die</creatorcontrib><creatorcontrib>Chen, Yuzhu</creatorcontrib><creatorcontrib>Fang, Yongchun</creatorcontrib><title>Programming-Based Optimal Learning Sliding Mode Control for Cooperative Dual Ship-Mounted Cranes Against Unmatched External Disturbances</title><title>IEEE transactions on automation science and engineering</title><addtitle>TASE</addtitle><description>When lifting and transporting large payloads in the marine environment, the dual ship-mounted crane system plays a very important role for cargos transportation, which exhibits strong load capacity and high flexibility. However, apart from the nonlinearity and underactuation characteristic, some unknown or uncertain unmatched wave disturbances may also cause positioning errors, which may induce various risks during the transportation process; besides, lots of existing methods ignore a part of the cooperative crane motions, and the control issue of dual ship-mounted crane system with five degrees of freedoms (5 DOF) is still open. In terms of the aforementioned problems, an adaptive dynamic programming (ADP)-based optimal learning sliding mode controller is proposed in this paper. Specifically, under the frame of adaptive dynamic programming, the Hamilton-Jacobi-Bellman (HJB) equation can be addressed. Then, based on the gradient attenuation algorithm, critic neural networks (NNs) are trained depending on the designed updating law and the approximate optimal learning controller can be obtained. Lyapunov techniques and Lasalle's invariance principle are used to guarantee asymptotic stability of the dual ship-mounted cranes system. Finally, a series of simulation results are depicted to show the effectiveness of the proposed optimal learning sliding mode controller. Note to Practitioners-In this paper, the control problem of a 5 DOF dual ship-mounted cranes system with unmatched wave disturbance is studied. Due to the complex nonlinear characteristics of the dual ship-mounted cranes and the need to consider the cooperative lifting and wave disturbances, the operation of the dual ship-mounted cranes are very challenging. Moreover, it brings more difficulties to the design of this kind of controller due to its nonlinear underactuated property. Existing control methods for dual ship-mounted cranes are based on linearized or oversimplified crane models, or require accurate model. To solve these kinds of problems, this paper proposes a new control approach for dual ship-mounted cranes suffering from unmatched wave disturbances to achieve satisfactory performance. The stability analysis of the closed-loop systems equilibrium point is implemented by Lyapunov techniques, implying that the accurate positioning and fast payload swings suppression against unmatched wave disturbances are achieved concurrently. In the future studies, we will apply the proposed control method to industrial dual ship-mounted cranes systems to improve their working safety and efficiency.</description><subject>adaptive dynamic programming</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Closed loops</subject><subject>Control methods</subject><subject>Controllers</subject><subject>Cooperative control</subject><subject>Cranes</subject><subject>Cranes & hoists</subject><subject>critic neural network</subject><subject>Disturbances</subject><subject>Dual ship-mounted cranes system</subject><subject>Dynamic programming</subject><subject>Feedback control</subject><subject>Learning</subject><subject>Marine environment</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Nonlinear dynamical systems</subject><subject>nonlinear system</subject><subject>Nonlinearity</subject><subject>Optimal control</subject><subject>optimal learning sliding mode control</subject><subject>Payloads</subject><subject>Robots</subject><subject>Sliding mode control</subject><subject>Stability analysis</subject><subject>Transportation</subject><issn>1545-5955</issn><issn>1558-3783</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF9LwzAUxYsoOKcfQHwJ-NyZP22TPM5t_oGNCdueS9qmW0ab1CQV_QZ-bFM2fLqHe885XH5RdI_gBCHIn7bTzWKCIcYTghimGF5EI5SmLCaUkctBJ2mc8jS9jm6cO0KIE8bhKPr9sGZvRdsqvY-fhZMVWHdetaIBSymsDmuwaVQ1zJWpJJgZ7a1pQG1s0KaTVnj1JcG8D5HNQXXxyvTah56ZFVo6MN0LpZ0HO90KXx7CYfHtpdXBPlfO97YQupTuNrqqRePk3XmOo93LYjt7i5fr1_fZdBmXmBMf14mgGS1hDUtBcV3QIkEE16wWGYcZ4hhKQQpGIWWsSLIEozQraClgWnHEE0rG0eOpt7Pms5fO50fTD9-4HFNOMcsyCoMLnVylNc5ZWeedDVDsT45gPgDPB-D5ADw_Aw-Zh1NGSSn__TyUUoLIH2QUfXM</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Qian, Yuzhe</creator><creator>Hu, Die</creator><creator>Chen, Yuzhu</creator><creator>Fang, Yongchun</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9734-2132</orcidid><orcidid>https://orcid.org/0000-0001-8084-5799</orcidid><orcidid>https://orcid.org/0000-0002-1564-6272</orcidid><orcidid>https://orcid.org/0000-0002-3061-2708</orcidid></search><sort><creationdate>20230401</creationdate><title>Programming-Based Optimal Learning Sliding Mode Control for Cooperative Dual Ship-Mounted Cranes Against Unmatched External Disturbances</title><author>Qian, Yuzhe ; Hu, Die ; Chen, Yuzhu ; Fang, Yongchun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-f4a767c0f0ca72fb7b4132f8fa69061920ea3b870788b4642156b7ca05d919473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>adaptive dynamic programming</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Closed loops</topic><topic>Control methods</topic><topic>Controllers</topic><topic>Cooperative control</topic><topic>Cranes</topic><topic>Cranes & hoists</topic><topic>critic neural network</topic><topic>Disturbances</topic><topic>Dual ship-mounted cranes system</topic><topic>Dynamic programming</topic><topic>Feedback control</topic><topic>Learning</topic><topic>Marine environment</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Nonlinear dynamical systems</topic><topic>nonlinear system</topic><topic>Nonlinearity</topic><topic>Optimal control</topic><topic>optimal learning sliding mode control</topic><topic>Payloads</topic><topic>Robots</topic><topic>Sliding mode control</topic><topic>Stability analysis</topic><topic>Transportation</topic><toplevel>online_resources</toplevel><creatorcontrib>Qian, Yuzhe</creatorcontrib><creatorcontrib>Hu, Die</creatorcontrib><creatorcontrib>Chen, Yuzhu</creatorcontrib><creatorcontrib>Fang, Yongchun</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on automation science and engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Qian, Yuzhe</au><au>Hu, Die</au><au>Chen, Yuzhu</au><au>Fang, Yongchun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Programming-Based Optimal Learning Sliding Mode Control for Cooperative Dual Ship-Mounted Cranes Against Unmatched External Disturbances</atitle><jtitle>IEEE transactions on automation science and engineering</jtitle><stitle>TASE</stitle><date>2023-04-01</date><risdate>2023</risdate><volume>20</volume><issue>2</issue><spage>969</spage><epage>980</epage><pages>969-980</pages><issn>1545-5955</issn><eissn>1558-3783</eissn><coden>ITASC7</coden><abstract>When lifting and transporting large payloads in the marine environment, the dual ship-mounted crane system plays a very important role for cargos transportation, which exhibits strong load capacity and high flexibility. However, apart from the nonlinearity and underactuation characteristic, some unknown or uncertain unmatched wave disturbances may also cause positioning errors, which may induce various risks during the transportation process; besides, lots of existing methods ignore a part of the cooperative crane motions, and the control issue of dual ship-mounted crane system with five degrees of freedoms (5 DOF) is still open. In terms of the aforementioned problems, an adaptive dynamic programming (ADP)-based optimal learning sliding mode controller is proposed in this paper. Specifically, under the frame of adaptive dynamic programming, the Hamilton-Jacobi-Bellman (HJB) equation can be addressed. Then, based on the gradient attenuation algorithm, critic neural networks (NNs) are trained depending on the designed updating law and the approximate optimal learning controller can be obtained. Lyapunov techniques and Lasalle's invariance principle are used to guarantee asymptotic stability of the dual ship-mounted cranes system. Finally, a series of simulation results are depicted to show the effectiveness of the proposed optimal learning sliding mode controller. Note to Practitioners-In this paper, the control problem of a 5 DOF dual ship-mounted cranes system with unmatched wave disturbance is studied. Due to the complex nonlinear characteristics of the dual ship-mounted cranes and the need to consider the cooperative lifting and wave disturbances, the operation of the dual ship-mounted cranes are very challenging. Moreover, it brings more difficulties to the design of this kind of controller due to its nonlinear underactuated property. Existing control methods for dual ship-mounted cranes are based on linearized or oversimplified crane models, or require accurate model. To solve these kinds of problems, this paper proposes a new control approach for dual ship-mounted cranes suffering from unmatched wave disturbances to achieve satisfactory performance. The stability analysis of the closed-loop systems equilibrium point is implemented by Lyapunov techniques, implying that the accurate positioning and fast payload swings suppression against unmatched wave disturbances are achieved concurrently. In the future studies, we will apply the proposed control method to industrial dual ship-mounted cranes systems to improve their working safety and efficiency.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TASE.2022.3182720</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-9734-2132</orcidid><orcidid>https://orcid.org/0000-0001-8084-5799</orcidid><orcidid>https://orcid.org/0000-0002-1564-6272</orcidid><orcidid>https://orcid.org/0000-0002-3061-2708</orcidid></addata></record> |
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subjects | adaptive dynamic programming Algorithms Artificial neural networks Closed loops Control methods Controllers Cooperative control Cranes Cranes & hoists critic neural network Disturbances Dual ship-mounted cranes system Dynamic programming Feedback control Learning Marine environment Mathematical models Neural networks Nonlinear dynamical systems nonlinear system Nonlinearity Optimal control optimal learning sliding mode control Payloads Robots Sliding mode control Stability analysis Transportation |
title | Programming-Based Optimal Learning Sliding Mode Control for Cooperative Dual Ship-Mounted Cranes Against Unmatched External Disturbances |
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