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.
@article{arxiv.1410.5083,
title = {Stability for Receding-horizon Stochastic Model Predictive Control},
author = {Joel A. Paulson and Stefan Streif and Ali Mesbah},
journal= {arXiv preprint arXiv:1410.5083},
year = {2015}
}