Robust Convex Model Predictive Control for Quadruped Locomotion Under Uncertainties
This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust...
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| Veröffentlicht in: | IEEE transactions on robotics 2023-12, Vol.39 (6), p.1-18 |
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| creator | Xu, Shaohang Zhu, Lijun Zhang, Hai-Tao Ho, Chin Pang |
| description | This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by \sim11× compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments. |
| doi_str_mv | 10.1109/TRO.2023.3299527 |
| format | Article |
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Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by <inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>11× compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments.</description><identifier>ISSN: 1552-3098</identifier><identifier>EISSN: 1941-0468</identifier><identifier>DOI: 10.1109/TRO.2023.3299527</identifier><identifier>CODEN: ITREAE</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptation models ; Algorithms ; Constraint modelling ; Controllers ; Heuristic algorithms ; Legged robots ; Locomotion ; model predictive control (MPC) ; Optimization ; optimization and optimal control ; Optimization models ; Predictive control ; Predictive models ; Quadratic programming ; Quadrupedal robots ; Robots ; Robust control ; robust/adaptive control of robotic systems ; Uncertainty ; Unknown environments</subject><ispartof>IEEE transactions on robotics, 2023-12, Vol.39 (6), p.1-18</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c292t-81638b5e70a50c1b73b869ba752070340e434a81d68f83d622ea628e273cd76b3</citedby><cites>FETCH-LOGICAL-c292t-81638b5e70a50c1b73b869ba752070340e434a81d68f83d622ea628e273cd76b3</cites><orcidid>0000-0002-8819-8829 ; 0000-0002-6157-242X ; 0000-0002-2655-1818 ; 0000-0002-2143-978X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10214438$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27931,27932,54765</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10214438$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Xu, Shaohang</creatorcontrib><creatorcontrib>Zhu, Lijun</creatorcontrib><creatorcontrib>Zhang, Hai-Tao</creatorcontrib><creatorcontrib>Ho, Chin Pang</creatorcontrib><title>Robust Convex Model Predictive Control for Quadruped Locomotion Under Uncertainties</title><title>IEEE transactions on robotics</title><addtitle>TRO</addtitle><description>This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by <inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>11× compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments.</description><subject>Adaptation models</subject><subject>Algorithms</subject><subject>Constraint modelling</subject><subject>Controllers</subject><subject>Heuristic algorithms</subject><subject>Legged robots</subject><subject>Locomotion</subject><subject>model predictive control (MPC)</subject><subject>Optimization</subject><subject>optimization and optimal control</subject><subject>Optimization models</subject><subject>Predictive control</subject><subject>Predictive models</subject><subject>Quadratic programming</subject><subject>Quadrupedal robots</subject><subject>Robots</subject><subject>Robust control</subject><subject>robust/adaptive control of robotic systems</subject><subject>Uncertainty</subject><subject>Unknown environments</subject><issn>1552-3098</issn><issn>1941-0468</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1LAzEQhoMoWKt3Dx4WPG-dfOwmOUrxCyrV2p5DdjMLW9pNTbJF_71b2oOXmWF43hl4CLmlMKEU9MNyMZ8wYHzCmdYFk2dkRLWgOYhSnQ9zUbCcg1aX5CrGNQATGviIfC181ceUTX23x5_s3TvcZB8BXVundo-HfQp-kzU-ZJ-9daHfoctmvvZbn1rfZavOYRhqjSHZtkstxmty0dhNxJtTH5PV89Ny-prP5i9v08dZXjPNUq5oyVVVoARbQE0ryStV6srKgoEELgAFF1ZRV6pGcVcyhrZkCpnktZNlxcfk_nh3F_x3jzGZte9DN7w0TGktJAVJBwqOVB18jAEbswvt1oZfQ8Ec1JlBnTmoMyd1Q-TuGGkR8R_OqBBc8T8ZQmnG</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Xu, Shaohang</creator><creator>Zhu, Lijun</creator><creator>Zhang, Hai-Tao</creator><creator>Ho, Chin Pang</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/0000-0002-8819-8829</orcidid><orcidid>https://orcid.org/0000-0002-6157-242X</orcidid><orcidid>https://orcid.org/0000-0002-2655-1818</orcidid><orcidid>https://orcid.org/0000-0002-2143-978X</orcidid></search><sort><creationdate>20231201</creationdate><title>Robust Convex Model Predictive Control for Quadruped Locomotion Under Uncertainties</title><author>Xu, Shaohang ; Zhu, Lijun ; Zhang, Hai-Tao ; Ho, Chin Pang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c292t-81638b5e70a50c1b73b869ba752070340e434a81d68f83d622ea628e273cd76b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adaptation models</topic><topic>Algorithms</topic><topic>Constraint modelling</topic><topic>Controllers</topic><topic>Heuristic algorithms</topic><topic>Legged robots</topic><topic>Locomotion</topic><topic>model predictive control (MPC)</topic><topic>Optimization</topic><topic>optimization and optimal control</topic><topic>Optimization models</topic><topic>Predictive control</topic><topic>Predictive models</topic><topic>Quadratic programming</topic><topic>Quadrupedal robots</topic><topic>Robots</topic><topic>Robust control</topic><topic>robust/adaptive control of robotic systems</topic><topic>Uncertainty</topic><topic>Unknown environments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Shaohang</creatorcontrib><creatorcontrib>Zhu, Lijun</creatorcontrib><creatorcontrib>Zhang, Hai-Tao</creatorcontrib><creatorcontrib>Ho, Chin Pang</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 robotics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xu, Shaohang</au><au>Zhu, Lijun</au><au>Zhang, Hai-Tao</au><au>Ho, Chin Pang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust Convex Model Predictive Control for Quadruped Locomotion Under Uncertainties</atitle><jtitle>IEEE transactions on robotics</jtitle><stitle>TRO</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>39</volume><issue>6</issue><spage>1</spage><epage>18</epage><pages>1-18</pages><issn>1552-3098</issn><eissn>1941-0468</eissn><coden>ITREAE</coden><abstract>This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by <inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>11× compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TRO.2023.3299527</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-8819-8829</orcidid><orcidid>https://orcid.org/0000-0002-6157-242X</orcidid><orcidid>https://orcid.org/0000-0002-2655-1818</orcidid><orcidid>https://orcid.org/0000-0002-2143-978X</orcidid></addata></record> |
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| subjects | Adaptation models Algorithms Constraint modelling Controllers Heuristic algorithms Legged robots Locomotion model predictive control (MPC) Optimization optimization and optimal control Optimization models Predictive control Predictive models Quadratic programming Quadrupedal robots Robots Robust control robust/adaptive control of robotic systems Uncertainty Unknown environments |
| title | Robust Convex Model Predictive Control for Quadruped Locomotion Under Uncertainties |
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