Title :
A soft constraint approach to stochastic receding horizon control
Author :
Primbs, James A.
Author_Institution :
Stanford Univ., Stanford
Abstract :
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.
Keywords :
asymptotic stability; constraint theory; linear systems; predictive control; probability; stochastic systems; asymptotic stability; constrained stochastic receding horizon control; constraint violation; control multiplicative noise; linear system; online optimization; probability; semidefinite program; soft constraint; state multiplicative noise; Asymptotic stability; Constraint optimization; Control systems; Linear systems; Open loop systems; State feedback; Stochastic processes; Stochastic resonance; Stochastic systems; Uncertainty;
Conference_Titel :
Decision and Control, 2007 46th IEEE Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
978-1-4244-1497-0
Electronic_ISBN :
0191-2216
DOI :
10.1109/CDC.2007.4434064
