Minimizing control variation in nonlinear optimal control
In any real system, changing the control signal from one value to another will usually cause wear and tear on the system’s actuators. Thus, when designing a control law, it is important to consider not just predicted system performance, but also the cost associated with changing the control action....
| Main Authors: | , , |
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| Format: | Journal Article |
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Pergamon
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/4801 |
| _version_ | 1848744618602004480 |
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| author | Loxton, Ryan Lin, Qun Teo, Kok Lay |
| author_facet | Loxton, Ryan Lin, Qun Teo, Kok Lay |
| author_sort | Loxton, Ryan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In any real system, changing the control signal from one value to another will usually cause wear and tear on the system’s actuators. Thus, when designing a control law, it is important to consider not just predicted system performance, but also the cost associated with changing the control action. This latter cost is almost always ignored in the optimal control literature. In this paper, we consider a class of optimal control problems in which the variation of the control signal is explicitly penalized in the cost function. We develop an effective computational method, based on the control parameterization approach and a novel transformation procedure, for solving this class of optimal control problems. We then apply our method to three example problems in fisheries, train control, and chemical engineering. |
| first_indexed | 2025-11-14T06:04:20Z |
| format | Journal Article |
| id | curtin-20.500.11937-4801 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:04:20Z |
| publishDate | 2013 |
| publisher | Pergamon |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-48012019-02-19T04:26:46Z Minimizing control variation in nonlinear optimal control Loxton, Ryan Lin, Qun Teo, Kok Lay total variation nonlinear optimization optimal control computation constrained optimal control In any real system, changing the control signal from one value to another will usually cause wear and tear on the system’s actuators. Thus, when designing a control law, it is important to consider not just predicted system performance, but also the cost associated with changing the control action. This latter cost is almost always ignored in the optimal control literature. In this paper, we consider a class of optimal control problems in which the variation of the control signal is explicitly penalized in the cost function. We develop an effective computational method, based on the control parameterization approach and a novel transformation procedure, for solving this class of optimal control problems. We then apply our method to three example problems in fisheries, train control, and chemical engineering. 2013 Journal Article http://hdl.handle.net/20.500.11937/4801 10.1016/j.automatica.2013.05.027 Pergamon fulltext |
| spellingShingle | total variation nonlinear optimization optimal control computation constrained optimal control Loxton, Ryan Lin, Qun Teo, Kok Lay Minimizing control variation in nonlinear optimal control |
| title | Minimizing control variation in nonlinear optimal control |
| title_full | Minimizing control variation in nonlinear optimal control |
| title_fullStr | Minimizing control variation in nonlinear optimal control |
| title_full_unstemmed | Minimizing control variation in nonlinear optimal control |
| title_short | Minimizing control variation in nonlinear optimal control |
| title_sort | minimizing control variation in nonlinear optimal control |
| topic | total variation nonlinear optimization optimal control computation constrained optimal control |
| url | http://hdl.handle.net/20.500.11937/4801 |