Title :
Stochastic Model Predictive Control of constrained linear systems with additive uncertainty
Author :
Magni, Lalo ; Pala, Daniele ; Scattolini, Riccardo
Author_Institution :
Univ. of Pavia, Pavia, Italy
Abstract :
This paper illustrates a stochastic Model Predictive Control (MPC) algorithm to control a linear system subject to additive zero-mean noise, state and input constraints. The algorithm proposed is computationally efficient since it can be formulated as a SemiDefinite Programming (SDP) problem and can thus be solved by interior-point methods. We also show that, under the hypotesis of bounded noise, the closed-loop system can be rendered Input-to-State-Stable (ISS).
Keywords :
closed loop systems; linear systems; mathematical programming; predictive control; stochastic processes; MPC algorithm; SDP problem; additive uncertainty; bounded noise; closed-loop system; constrained linear systems; input constraints; input-to-state-stable closed-loop system; interior-point methods; semidefinite programming problem; state constraints; stochastic model predictive control; zero-mean noise; Closed loop systems; Linear systems; Noise; Optimization; Probabilistic logic; Robustness; Stochastic processes;
Conference_Titel :
Control Conference (ECC), 2009 European
Conference_Location :
Budapest
Print_ISBN :
978-3-9524173-9-3
