Minimizing control variation in nonlinear optimal control

Authors:
Ryan Loxton;Qun Lin;Kok Lay Teo
Affiliations:
-;-;-
Venue:
Automatica (Journal of IFAC)
Year:
2013

Citing 11
Cited 0

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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.