Abstract
Markov chain Monte Carlo methods can be used to make optimal decisions in very complex situations in which stochastic effects are prominent. We argue that these methods can be viewed as providing a class of nonlinear MPC methods. We discuss decision taking by maximising expected utility, and give an extension which allows constraints to be respected. We give a brief account of an application to air traffic control, and point out some other problem areas which appear to be very amenable to solution by the same approach.
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Maciejowski, J.M., Visintini, A.L., Lygeros, J. (2007). NMPC for Complex Stochastic Systems Using a Markov Chain Monte Carlo Approach. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_21
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DOI: https://doi.org/10.1007/978-3-540-72699-9_21
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