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A soft constraint approach to stochastic receding horizon control

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1 Author(s)
Primbs, James A. ; Stanford Univ., Stanford

This paper presents a soft constraint approach to constrained stochastic receding horizon control for linear systems with state and control multiplicative noise. We formulate an on-line optimization that penalizes constraint violations and can be solved as a semi-definite program. Additionally, we prove stability results that guarantee asymptotic stability with probability one. A simple numerical example illustrates the approach.

Published in:

Decision and Control, 2007 46th IEEE Conference on

Date of Conference:

12-14 Dec. 2007