Control For Energy and Sustainability

EPSRC Programme Grant

[YKLM10] Z.Yang, N.Kantas, A.Lecchini-Visintini and J.M.Maciejowski, Stochastic model predictive control using open loop annealing and Monte Carlo methods, Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS), Budapest Hungary, Jul., 2010


In this paper we investigate the use of Model Predictive Control for Markov Decision Processes under weak assumptions. We provide conditions for stability based on optimality of a specific class of cost functions. These results are useful from both a theoretical and computational perspective. When nonlinear non-Gaussian models for general state spaces are considered, the absence of analytical tools makes the use of simulation based methods necessary. Popular simulation based methods like stochastic programming and Markov Chain Monte Carlo can be used to provide open loop estimates of the optimisers. With this in mind we provide conditions under which such an approach would yield stable Markov Decision Processes.