Stability for receding-horizon stochastic model predictive control

Author

Paulson, Joel A. ; Streif, Stefan ; Mesbah, Ali

Author_Institution

Dept. of Chem. & Biomol. Eng., Univ. of California, Berkeley, Berkeley, CA, USA

fYear

2015

fDate

1-3 July 2015

Firstpage

937

Lastpage

943

Abstract

A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems with arbitrary time-invariant probabilistic uncertainties and additive Gaussian process noise. Closed-loop stability of the SMPC approach is established by appropriate selection of the cost function. Polynomial chaos is used for uncertainty propagation through system dynamics. The performance of the SMPC approach is demonstrated using the Van de Vusse reactions.

Keywords

Gaussian processes; discrete time systems; linear systems; polynomials; predictive control; stability; stochastic systems; uncertain systems; SMPC approach; Van de Vusse reactions; additive Gaussian process noise; arbitrary time-invariant probabilistic uncertainties; closed-loop stability; cost function; discrete-time linear systems; polynomial chaos; receding horizon stochastic model predictive control; stability; uncertainty propagation; Approximation methods; Noise; Polynomials; Probabilistic logic; Stability analysis; Stochastic processes; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference (ACC), 2015

Conference_Location

Chicago, IL

Print_ISBN

978-1-4799-8685-9

Type

conf

DOI

10.1109/ACC.2015.7170854

Filename

7170854