Asymptotic optimization of a nonlinear hybrid system governed by a Markov decision process

We consider in this paper a continuous time stochastic hybrid control system with finite time horizon. The objective is to minimize a nonlinear function of the state trajectory. The state evolves according to a nonlinear dynamics. The parameters of the dynamics of the system may change at discrete times l epsilon, l = 0, 1, ..., according to a controlled Markov chain which has finite state and action spaces. Under the assumption that epsilon is a small parameter, we justify an averaging procedure allowing us to establish that our problem can be approximated by the solution of some deterministic optimal control problem.