Title :
A fast algorithm for stochastic model predictive control with probabilistic constraints
Author :
Minyong Shin ; Primbs, J.A.
Author_Institution :
Dept. of Aeronaut. & Astronaut., Stanford Univ., Stanford, CA, USA
fDate :
June 30 2010-July 2 2010
Abstract :
A fast suboptimal algorithm for finite horizon stochastic linear-quadratic control under probabilistic constraints is presented. This type of control problem is solved repeatedly in stochastic model predictive control. Under the assumption of affine state feedback, the control problem is converted to an equivalent deterministic problem using the mean and covariance matrix as the state. An interior point method is proposed to solve this optimization problem, where the step direction can be quickly computed via a Riccati difference equation. On a two state, two constraint numerical example in this paper, the algorithm is over 200 times faster than a convex formulation that uses a general purpose solver when the time horizon is 25.
Keywords :
Riccati equations; covariance matrices; difference equations; linear quadratic control; optimisation; predictive control; state feedback; stochastic systems; Riccati difference equation; affine state feedback; convex formulation; covariance matrix; equivalent deterministic problem; finite horizon stochastic linear-quadratic control; interior point method; optimization problem; probabilistic constraints; stochastic model predictive control; Control systems; Covariance matrix; Difference equations; Optimization methods; Predictive control; Predictive models; Riccati equations; State feedback; Stochastic processes; Stochastic resonance;
Conference_Titel :
American Control Conference (ACC), 2010
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-7426-4
DOI :
10.1109/ACC.2010.5530970
