Abstract
The article develops a reinforcement learning control scheme for a two-degree-of-freedom (2-DOF) helicopter system to achieve robust tracking. The control framework is divided into two parts: the critic neural network (NN) and the actor NN, which are designed to evaluate the control performance and estimate system uncertainties, respectively. Besides, the gradient descent method is exploited to update the weight of radial basis function NNs. Under the proposed control strategy, the rigorous stability of the closed-loop system is analyzed and demonstrated by Lyapunov’s stability theory. Finally, the Matlab simulation results are provided to verify the efficacy of the suggested scheme.
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