Title :
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
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;
Conference_Titel :
American Control Conference (ACC), 2015
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4799-8685-9
DOI :
10.1109/ACC.2015.7170854
