Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances
Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QP in real-time can be a computationall...
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| creator | Yaghoubi, Shakiba Fainekos, Georgios Sankaranarayanan, Sriram |
| description | Control Barrier Functions (CBF) have been recently utilized in the design of
provably safe feedback control laws for nonlinear systems. These feedback
control methods typically compute the next control input by solving an online
Quadratic Program (QP). Solving QP in real-time can be a computationally
expensive process for resource constraint systems. In this work, we propose to
use imitation learning to learn Neural Network-based feedback controllers which
will satisfy the CBF constraints. In the process, we also develop a new class
of High Order CBF for systems under external disturbances. We demonstrate the
framework on a unicycle model subject to external disturbances, e.g., wind or
currents. |
| doi_str_mv | 10.48550/arxiv.2001.08088 |
| format | Article |
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provably safe feedback control laws for nonlinear systems. These feedback
control methods typically compute the next control input by solving an online
Quadratic Program (QP). Solving QP in real-time can be a computationally
expensive process for resource constraint systems. In this work, we propose to
use imitation learning to learn Neural Network-based feedback controllers which
will satisfy the CBF constraints. In the process, we also develop a new class
of High Order CBF for systems under external disturbances. We demonstrate the
framework on a unicycle model subject to external disturbances, e.g., wind or
currents.</description><identifier>DOI: 10.48550/arxiv.2001.08088</identifier><language>eng</language><subject>Computer Science - Learning ; Computer Science - Systems and Control ; Mathematics - Optimization and Control ; Statistics - Machine Learning</subject><creationdate>2020-01</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2001.08088$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2001.08088$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Yaghoubi, Shakiba</creatorcontrib><creatorcontrib>Fainekos, Georgios</creatorcontrib><creatorcontrib>Sankaranarayanan, Sriram</creatorcontrib><title>Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances</title><description>Control Barrier Functions (CBF) have been recently utilized in the design of
provably safe feedback control laws for nonlinear systems. These feedback
control methods typically compute the next control input by solving an online
Quadratic Program (QP). Solving QP in real-time can be a computationally
expensive process for resource constraint systems. In this work, we propose to
use imitation learning to learn Neural Network-based feedback controllers which
will satisfy the CBF constraints. In the process, we also develop a new class
of High Order CBF for systems under external disturbances. We demonstrate the
framework on a unicycle model subject to external disturbances, e.g., wind or
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provably safe feedback control laws for nonlinear systems. These feedback
control methods typically compute the next control input by solving an online
Quadratic Program (QP). Solving QP in real-time can be a computationally
expensive process for resource constraint systems. In this work, we propose to
use imitation learning to learn Neural Network-based feedback controllers which
will satisfy the CBF constraints. In the process, we also develop a new class
of High Order CBF for systems under external disturbances. We demonstrate the
framework on a unicycle model subject to external disturbances, e.g., wind or
currents.</abstract><doi>10.48550/arxiv.2001.08088</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Learning Computer Science - Systems and Control Mathematics - Optimization and Control Statistics - Machine Learning |
| title | Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances |
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