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Path Constraints in Tychastic and Unscented Optimal Control: Theory, Applications and Experimental Results

I. M. Ross(imross***at***nps.edu)
M. Karpenko(mkarpenk***at***nps.edu)
R. J. Proulx(rjproulx***at***nps.edu)

Abstract: In recent papers, we have shown that a Lebesgue-Stieltjes optimal control theory forms the foundations for unscented optimal control. In this paper, we further our results by incorporating uncertain mixed state-control constraints in the problem formulation. We show that the integrated Hamiltonian minimization condition resembles a semi-infinite type mathematical programming problem. The resulting computational difficulties are mitigated through the use of the unscented transform; however, the price of this approximation is a solution to a chance-constrained optimal control problem whose risk level is determined a posteriori. Experimental results conducted at Honeywell are presented to demonstrate the success of the theory. An order of magnitude reduction in the failure rate in obtained through the use of an unscented optimal control that steers a spacecraft testbed driven by control-moment gyros.

Keywords: Stochastic Optimal Control, Unscented Transform, DIDO optimal control toolbox

Category 1: Stochastic Programming

Category 2: Robust Optimization

Category 3: Applications -- Science and Engineering

Citation: 2016 American Control Conference, July 6-8, 2016,Boston, MA

Download: [PDF]

Entry Submitted: 06/06/2016
Entry Accepted: 06/07/2016
Entry Last Modified: 06/06/2016

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