Name
Abstract | ||
|---|---|---|
The problem of controlling an LPV system subject to both hard and probabilistic constraints is considered. A necessary and sufficient condition for the inclusion of a polytope within another polytope, which is defined in terms of random variables, is given. This leads naturally to a tube MPC optimisation including linear probabilistic constraints. This can be solved approximately by considering a related problem obtained by sampling, which gives a mixed integer programming problem with a convex continuous relaxation. For fast sampling applications, we outline efficient approximate methods of solving the MIP via greedy constraint removal. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CDC.2014.7040135 | Decision and Control |
Keywords | Field | DocType |
integer programming,linear parameter varying systems,predictive control,probability,LPV system,MIP,convex continuous relaxation,efficient approximate methods,greedy constraint removal,linear probabilistic constraints,mixed integer programming problem,probabilistic set inclusion conditions,receding horizon model predictive control,stochastic tube MPC optimisation | Mathematical optimization,Random variable,Computer science,Control theory,Regular polygon,Integer programming,Polytope,Sampling (statistics),Probabilistic logic | Conference |
ISSN | ISBN | Citations |
0743-1546 | 978-1-4799-7746-8 | 5 |
PageRank | References | Authors |
0.50 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fleming, J. | 1 | 12 | 3.22 |
| Mark Cannon | 2 | 511 | 63.73 |
| Basil Kouvaritakis | 3 | 384 | 43.65 |