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Minimizing control variation in nonlinear optimal control

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Open access
Authors
Loxton, Ryan
Lin, Qun
Teo, Kok Lay
Date
2013
Type
Journal Article

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Loxton, Ryan and Lin, Qun and Teo, Kok Lay. 2013. Minimizing control variation in nonlinear optimal control. Automatica. 49 (9): pp. 2652-2664.
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Automatica
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NOTICE: This is the author’s version of a work that was accepted for publication in Automatica. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Automatica, Vol. 49, Issue 9, (2013). DOI: 10.1016/j.automatica.2013.05.027

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Abstract

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.