Chance-constrained LQG with bounded control policies

Author

Hokayem, Peter ; Chatterjee, Debangshu ; Lygeros, John

Author_Institution

Corp. Res. Center, ABB, Baden-Dättwil, Switzerland

fYear

2013

fDate

10-13 Dec. 2013

Firstpage

2471

Lastpage

2476

Abstract

We study the finite-horizon LQG problem in which the states are required to satisfy probabilistic constraints, and the control inputs are required to satisfy hard bounds. We demonstrate that a general class of feedback policies satisfying the above constraints can be algorithmically selected via the solution to a convex optimization problem. An estimate of the region of initial conditions for which the chance constraints are feasible is also provided. Our approach relies on concentration of measure inequalities for the standard Gaussian measure.

Keywords

Gaussian processes; constraint satisfaction problems; convex programming; feedback; infinite horizon; linear quadratic Gaussian control; probability; bounded control policies; chance-constrained LQG; constraint satisfaction; control inputs; convex optimization problem; feedback policies; finite-horizon LQG problem; probabilistic constraints; standard Gaussian measure; Approximation methods; Noise; Optimal control; Optimization; Standards; Stochastic processes; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on

Conference_Location

Firenze

ISSN

0743-1546

Print_ISBN

978-1-4673-5714-2

Type

conf

DOI

10.1109/CDC.2013.6760251

Filename

6760251