Tube-Based Discrete Controller Design for Vehicle Platoons Subject to Disturbances and Saturation Constraints
Cooperative adaptive cruise control (CACC) is a promising intelligent vehicle technology for improving traffic flow stability, throughput, and safety. One major control objective of CACC is to guarantee \mathcal {L}_{p} string stability, i.e., \mathcal {L}_{p} -norm measured disturbance is unifor...
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description | Cooperative adaptive cruise control (CACC) is a promising intelligent vehicle technology for improving traffic flow stability, throughput, and safety. One major control objective of CACC is to guarantee \mathcal {L}_{p} string stability, i.e., \mathcal {L}_{p} -norm measured disturbance is uniformly bounded along the vehicle string. Most existing methods for string stability are laborious for implementation without considering either heterogeneous disturbances (e.g., tracking errors and unmodeled dynamics) or saturation constraints (e.g., input saturation). The decentralized model predictive control (MPC) method, which is a widely used feedforward control for string stability, suffers the burdens of computation cost and intervehicular communication. To fill these gaps, we distinguish different types of disturbances and use different ways to handle them. We use feedforward control for large yet infrequent disturbances and feedback control for small yet frequent disturbances. Different from MPC, our feedforward control is event-triggered so that the intervehicle communication and planning costs can be significantly reduced. Different from pure robust feedback control, our combination of feedback and feedforward control could reduce the conservation of the controller. Theoretical analysis and simulations show that the proposed method guarantees \mathcal {L}_{p} string stability of vehicle platoons considering heterogeneous disturbances and saturation constraints. |
doi_str_mv | 10.1109/TCST.2019.2896539 |
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One major control objective of CACC is to guarantee <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula> string stability, i.e., <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula>-norm measured disturbance is uniformly bounded along the vehicle string. Most existing methods for string stability are laborious for implementation without considering either heterogeneous disturbances (e.g., tracking errors and unmodeled dynamics) or saturation constraints (e.g., input saturation). The decentralized model predictive control (MPC) method, which is a widely used feedforward control for string stability, suffers the burdens of computation cost and intervehicular communication. To fill these gaps, we distinguish different types of disturbances and use different ways to handle them. We use feedforward control for large yet infrequent disturbances and feedback control for small yet frequent disturbances. Different from MPC, our feedforward control is event-triggered so that the intervehicle communication and planning costs can be significantly reduced. Different from pure robust feedback control, our combination of feedback and feedforward control could reduce the conservation of the controller. Theoretical analysis and simulations show that the proposed method guarantees <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula> string stability of vehicle platoons considering heterogeneous disturbances and saturation constraints.]]></description><identifier>ISSN: 1063-6536</identifier><identifier>EISSN: 1558-0865</identifier><identifier>DOI: 10.1109/TCST.2019.2896539</identifier><identifier>CODEN: IETTE2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive control ; Computer simulation ; Control stability ; Control systems design ; Control theory ; Controllers ; Cooperative adaptive cruise control (CACC) ; Cooperative control ; Cruise control ; disturbance <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Lₚ string stability (DSS) ; Disturbances ; Electron tubes ; Feedback control ; Feedforward control ; Feedforward systems ; Flow stability ; Intelligent vehicles ; Mathematical model ; model predictive control ; Predictive control ; Robust control ; Saturation ; Stability criteria ; Strings ; Tracking errors ; Traffic flow ; Traffic safety ; tube ; Vehicle dynamics</subject><ispartof>IEEE transactions on control systems technology, 2020-05, Vol.28 (3), p.1066-1073</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-e6df63510e1d9c5a52abbc2cc1e7fe430e1df1782b3333e39f62b6c2f38cc7333</citedby><cites>FETCH-LOGICAL-c293t-e6df63510e1d9c5a52abbc2cc1e7fe430e1df1782b3333e39f62b6c2f38cc7333</cites><orcidid>0000-0002-2117-4427 ; 0000-0001-5526-866X ; 0000-0002-9428-1960 ; 0000-0002-3685-9920</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8643725$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8643725$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Feng, Shuo</creatorcontrib><creatorcontrib>Sun, Haowei</creatorcontrib><creatorcontrib>Zhang, Yi</creatorcontrib><creatorcontrib>Zheng, Jianfeng</creatorcontrib><creatorcontrib>Liu, Henry X.</creatorcontrib><creatorcontrib>Li, Li</creatorcontrib><title>Tube-Based Discrete Controller Design for Vehicle Platoons Subject to Disturbances and Saturation Constraints</title><title>IEEE transactions on control systems technology</title><addtitle>TCST</addtitle><description><![CDATA[Cooperative adaptive cruise control (CACC) is a promising intelligent vehicle technology for improving traffic flow stability, throughput, and safety. One major control objective of CACC is to guarantee <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula> string stability, i.e., <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula>-norm measured disturbance is uniformly bounded along the vehicle string. Most existing methods for string stability are laborious for implementation without considering either heterogeneous disturbances (e.g., tracking errors and unmodeled dynamics) or saturation constraints (e.g., input saturation). The decentralized model predictive control (MPC) method, which is a widely used feedforward control for string stability, suffers the burdens of computation cost and intervehicular communication. To fill these gaps, we distinguish different types of disturbances and use different ways to handle them. We use feedforward control for large yet infrequent disturbances and feedback control for small yet frequent disturbances. Different from MPC, our feedforward control is event-triggered so that the intervehicle communication and planning costs can be significantly reduced. Different from pure robust feedback control, our combination of feedback and feedforward control could reduce the conservation of the controller. Theoretical analysis and simulations show that the proposed method guarantees <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula> string stability of vehicle platoons considering heterogeneous disturbances and saturation constraints.]]></description><subject>Adaptive control</subject><subject>Computer simulation</subject><subject>Control stability</subject><subject>Control systems design</subject><subject>Control theory</subject><subject>Controllers</subject><subject>Cooperative adaptive cruise control (CACC)</subject><subject>Cooperative control</subject><subject>Cruise control</subject><subject>disturbance <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Lₚ string stability (DSS)</subject><subject>Disturbances</subject><subject>Electron tubes</subject><subject>Feedback control</subject><subject>Feedforward control</subject><subject>Feedforward systems</subject><subject>Flow stability</subject><subject>Intelligent vehicles</subject><subject>Mathematical model</subject><subject>model predictive control</subject><subject>Predictive control</subject><subject>Robust control</subject><subject>Saturation</subject><subject>Stability criteria</subject><subject>Strings</subject><subject>Tracking errors</subject><subject>Traffic flow</subject><subject>Traffic safety</subject><subject>tube</subject><subject>Vehicle dynamics</subject><issn>1063-6536</issn><issn>1558-0865</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRSMEEqXwAYiNJdYpfsSOvYSWl1QJpAa2keNMIFVqF9tZ8Pc4asVsZubq3hnpZNk1wQtCsLqrlptqQTFRCyqV4EydZDPCucyxFPw0zViwPOniPLsIYYsxKTgtZ9muGhvIH3SAFq36YDxEQEtno3fDAB6tIPRfFnXOo0_47s0A6H3Q0Tkb0GZstmAiim6KxtE32hoISNsWbXTadeydna6F6HVvY7jMzjo9BLg69nn28fRYLV_y9dvz6_J-nRuqWMxBtJ1gnGAgrTJcc6qbxlBjCJQdFGzSO1JK2rBUwFQnaCMM7Zg0pkzSPLs93N179zNCiPXWjd6mlzVlUjFRSEaSixxcxrsQPHT13vc77X9rguuJaj1RrSeq9ZFqytwcMj0A_PulKFhJOfsDl8l1KA</recordid><startdate>202005</startdate><enddate>202005</enddate><creator>Feng, Shuo</creator><creator>Sun, Haowei</creator><creator>Zhang, Yi</creator><creator>Zheng, Jianfeng</creator><creator>Liu, Henry X.</creator><creator>Li, Li</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>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-2117-4427</orcidid><orcidid>https://orcid.org/0000-0001-5526-866X</orcidid><orcidid>https://orcid.org/0000-0002-9428-1960</orcidid><orcidid>https://orcid.org/0000-0002-3685-9920</orcidid></search><sort><creationdate>202005</creationdate><title>Tube-Based Discrete Controller Design for Vehicle Platoons Subject to Disturbances and Saturation Constraints</title><author>Feng, Shuo ; Sun, Haowei ; Zhang, Yi ; Zheng, Jianfeng ; Liu, Henry X. ; Li, Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-e6df63510e1d9c5a52abbc2cc1e7fe430e1df1782b3333e39f62b6c2f38cc7333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptive control</topic><topic>Computer simulation</topic><topic>Control stability</topic><topic>Control systems design</topic><topic>Control theory</topic><topic>Controllers</topic><topic>Cooperative adaptive cruise control (CACC)</topic><topic>Cooperative control</topic><topic>Cruise control</topic><topic>disturbance <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Lₚ string stability (DSS)</topic><topic>Disturbances</topic><topic>Electron tubes</topic><topic>Feedback control</topic><topic>Feedforward control</topic><topic>Feedforward systems</topic><topic>Flow stability</topic><topic>Intelligent vehicles</topic><topic>Mathematical model</topic><topic>model predictive control</topic><topic>Predictive control</topic><topic>Robust control</topic><topic>Saturation</topic><topic>Stability criteria</topic><topic>Strings</topic><topic>Tracking errors</topic><topic>Traffic flow</topic><topic>Traffic safety</topic><topic>tube</topic><topic>Vehicle dynamics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Feng, Shuo</creatorcontrib><creatorcontrib>Sun, Haowei</creatorcontrib><creatorcontrib>Zhang, Yi</creatorcontrib><creatorcontrib>Zheng, Jianfeng</creatorcontrib><creatorcontrib>Liu, Henry X.</creatorcontrib><creatorcontrib>Li, Li</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>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on control systems technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Feng, Shuo</au><au>Sun, Haowei</au><au>Zhang, Yi</au><au>Zheng, Jianfeng</au><au>Liu, Henry X.</au><au>Li, Li</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tube-Based Discrete Controller Design for Vehicle Platoons Subject to Disturbances and Saturation Constraints</atitle><jtitle>IEEE transactions on control systems technology</jtitle><stitle>TCST</stitle><date>2020-05</date><risdate>2020</risdate><volume>28</volume><issue>3</issue><spage>1066</spage><epage>1073</epage><pages>1066-1073</pages><issn>1063-6536</issn><eissn>1558-0865</eissn><coden>IETTE2</coden><abstract><![CDATA[Cooperative adaptive cruise control (CACC) is a promising intelligent vehicle technology for improving traffic flow stability, throughput, and safety. One major control objective of CACC is to guarantee <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula> string stability, i.e., <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula>-norm measured disturbance is uniformly bounded along the vehicle string. Most existing methods for string stability are laborious for implementation without considering either heterogeneous disturbances (e.g., tracking errors and unmodeled dynamics) or saturation constraints (e.g., input saturation). The decentralized model predictive control (MPC) method, which is a widely used feedforward control for string stability, suffers the burdens of computation cost and intervehicular communication. To fill these gaps, we distinguish different types of disturbances and use different ways to handle them. We use feedforward control for large yet infrequent disturbances and feedback control for small yet frequent disturbances. Different from MPC, our feedforward control is event-triggered so that the intervehicle communication and planning costs can be significantly reduced. Different from pure robust feedback control, our combination of feedback and feedforward control could reduce the conservation of the controller. Theoretical analysis and simulations show that the proposed method guarantees <inline-formula> <tex-math notation="LaTeX">\mathcal {L}_{p} </tex-math></inline-formula> string stability of vehicle platoons considering heterogeneous disturbances and saturation constraints.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCST.2019.2896539</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-2117-4427</orcidid><orcidid>https://orcid.org/0000-0001-5526-866X</orcidid><orcidid>https://orcid.org/0000-0002-9428-1960</orcidid><orcidid>https://orcid.org/0000-0002-3685-9920</orcidid></addata></record> |
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subjects | Adaptive control Computer simulation Control stability Control systems design Control theory Controllers Cooperative adaptive cruise control (CACC) Cooperative control Cruise control disturbance <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Lₚ string stability (DSS) Disturbances Electron tubes Feedback control Feedforward control Feedforward systems Flow stability Intelligent vehicles Mathematical model model predictive control Predictive control Robust control Saturation Stability criteria Strings Tracking errors Traffic flow Traffic safety tube Vehicle dynamics |
title | Tube-Based Discrete Controller Design for Vehicle Platoons Subject to Disturbances and Saturation Constraints |
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