Strongly feasible stochastic model predictive control

In this article we develop a systematic approach to enforce strong feasibility of probabilistically constrained stochastic model predictive control problems for linear discrete-time systems under affine disturbance feedback policies. Two approaches are presented, both of which capitalize and extend the machinery of invariant sets to a stochastic environment. The first approach employs an invariant set as a terminal constraint, whereas the second one constrains the first predicted state. Consequently, the second approach turns out to be completely independent of the policy in question and moreover it produces the largest feasible set amongst all admissible policies. As a result, a trade-off between computational complexity and performance can be found without compromising feasibility properties. Our results are demonstrated by means of two numerical examples.

Published in:
Proceedings of the 50th IEEE Conference on Decision and Control, 1245 - 1251
Presented at:
50th IEEE Conference on Decision and Control, Orlando, Florida, USA, December 12-15, 2011

 Record created 2012-03-16, last modified 2018-06-22

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