Stochastic MPC for Controlling the Average Constraint Violation for Periodic Linear System with Additive Disturbance

This paper deals with stochastic model predictive control of constrained discrete-time periodic linear systems. Control inputs are subject to periodically time-varying polytopic constraints with possibly time-dependent state and input dimensions. A stochastic constraint is instead enforced on the system state process imposing a bound on the average over time of state constraint violations. Disturbances are additive, bounded and described by a periodically time-dependent probabilistic distribution. The aim of this paper is to develop a receding horizon control scheme which enforces recursive feasibility for the closed-loop state process. The effectiveness of the proposed algorithm is finally shown through a simulation study on a building climate control case.


Published in:
Proceedings of the American Control Conference
Presented at:
American Control Conference, Boston, MA, USA, July 6-8, 2016
American Control Conference (ACC), 2016, Boston, MA, USA, 6-07, 2016
Year:
2016
Publisher:
New York, IEEE
ISSN:
0743-1619
ISBN:
978-1-4673-8682-1
Keywords:
Laboratories:




 Record created 2016-05-31, last modified 2018-03-17

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)