Helicopter velocity tracking control by adaptive actor-critic reinforcement method

A robotic helicopter is an aircraft equipped with a sensing, computing, actuation, and communication infrastructure that allows it to execute a variety of tasks with autonomous mode. In this paper, we present an adaptive actor-critic reinforcement method to obtain near optimal controller for small autonomous helicopter. A network based on Q-value performs the critic and is trained by SARSA algorithm. A BP neural network, which is the actor network, generates control signal of helicopter dynamics. First, the proposed actor-critic reinforcement controller is introduced, then the algorithm is applied to an unmanned helicopter known as a highly nonlinear and complex system and the simulation results are presented.