Abstract
In this paper, a new data-driven reinforcement learning method based on Monte Carlo simulation is developed to solve the optimal control problem of unmanned aerial vehicle (UAV) systems. Based on the data which are generated by Monte Carlo simulation, neural network (NN) is used to construct the dynamics of the UAV system with unknown disturbances, where the mathematical model of the UAV system is unnecessary. An effective iterative framework of action and critic is constructed to obtain the optimal control law. The convergence property is developed to guarantee that the iterative performance cost function converges to a finite neighborhood of the optimal performance cost function. Finally, numerical results are given to illustrate the effectiveness of the developed method.
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