  In this paper, we propose a data-driven approach for control of nonlinear
dynamical systems. The proposed data-driven approach relies on transfer Koopman
and Perron-Frobenius (P-F) operators for linear representation and control of
such systems. Systematic model-based frameworks involving linear transfer P-F
operator were proposed for almost everywhere stability analysis and control
design of a nonlinear dynamical system in previous works [1-3]. Lyapunov
measure can be used as a tool to provide linear programming-based computational
framework for stability analysis and almost everywhere stabilizing control
design of a nonlinear system. In this paper, we show that those frameworks can
be extended to a data-driven setting, where the finite dimensional
approximation of linear transfer P-F operator and stabilizing feedback
controller can be obtained from time-series data. We exploit the positivity and
Markov property of these operators and their finite-dimensional approximation
to provide {\it linear programming} based approach for designing an optimally
stabilizing feedback controller.
