Multilayer control of large Markov chains

The computational burden associated with controlling a plant modeled as a Markov chain with a large number of states is addressed by proposing a two-layer feedback control structure. At the lower layer a regulator continuously monitors the plant. When the state of the plant reaches an extreme value, the supervisor at the higher layer intervenes to reset the regulator. It is shown that the plant dynamics and cost originally defined at the lower layer can be "lifted" to the supervisor layer and that the supervisor\´s control task can be defined in a way that permits wide flexibility in the design of the regulator.