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Stability for Receding-horizon Stochastic Model Predictive Control
Published 19 Oct 2014 in cs.SY and math.OC | (1410.5083v2)
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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