Stability for Receding-horizon Stochastic Model Predictive Control
Joel A. Paulson, Stefan Streif, and Ali Mesbah
TL;DR
This paper introduces a stochastic model predictive control method for linear systems with probabilistic uncertainties, ensuring stability through cost function design and using polynomial chaos for uncertainty propagation, demonstrated on chemical reactions.
Contribution
It presents a novel SMPC framework with stability guarantees for systems with probabilistic uncertainties, utilizing polynomial chaos for uncertainty propagation.
Findings
Stability of the SMPC approach is established.
Polynomial chaos effectively propagates uncertainties.
Performance validated on Van de Vusse reactions.
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.
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Taxonomy
TopicsAdvanced Control Systems Optimization · Fault Detection and Control Systems · Control Systems and Identification