Name
Title | ||
|---|---|---|
Stochastic MPC for additive and multiplicative uncertainty using sample approximations |
Abstract | ||
|---|---|---|
We introduce an approach for Model Predictive Control (MPC) of systems with additive and multiplicative stochastic uncertainty subject to chance constraints. Predicted states are bounded within a tube and the chance constraint is considered in a u0027one step aheadu0027 manner, with robust constraints applied over the remainder of the horizon. The online optimization is formulated as a chance-constrained program which is solved approximately using sampling. We prove that if the optimization is initially feasible, it remains feasible and the closed-loop system is stable. Applying the chance-constraint only one step ahead allows us to state a confidence bound for satisfaction of the chance constraint in closed-loop. Finally, we demonstrate by example that the resulting controller is only mildly more conservative than scenario MPC approaches that have no feasibility guarantee. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TAC.2018.2887054 | IEEE Transactions on Automatic Control |
Keywords | Field | DocType |
Uncertainty,Electron tubes,Optimization,Stochastic processes,Additives,Predictive control,Uncertain systems | Control theory,Mathematical optimization,Multiplicative function,Control theory,Horizon,Model predictive control,Remainder,Stochastic process,Sampling (statistics),Mathematics,Bounded function | Journal |
Volume | Issue | ISSN |
64 | 9 | 0018-9286 |
Citations | PageRank | References |
2 | 0.37 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fleming, J. | 1 | 12 | 3.22 |
| Mark Cannon | 2 | 511 | 63.73 |