Name
Title | ||
|---|---|---|
Stochastic Model Predictive Control For Linear Systems Using Probabilistic Reachable Sets |
Abstract | ||
|---|---|---|
In this paper, we propose a stochastic model predictive control (MPC) algorithm for linear discrete-time systems affected by possibly unbounded additive disturbances and subject to probabilistic constraints. Constraints are treated in analogy to robust MPC using a constraint tightening based on the concept of probabilistic reachable sets, which is shown to provide closed-loop fulfillment of chance constraints under a unimodality assumption on the disturbance distribution. A control scheme reverting to a backup solution from a previous time step in case of infeasibility is proposed, for which an asymptotic average performance bound is derived. Two examples illustrate the approach, highlighting closed-loop chance constraint satisfaction and the benefits of the proposed controller in the presence of unmodeled disturbances. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CDC.2018.8619554 | 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC) |
DocType | Volume | ISSN |
Conference | abs/1805.07145 | 0743-1546 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukas Hewing | 1 | 25 | 4.27 |
| Melanie Nicole Zeilinger | 2 | 298 | 30.91 |