Development of Reinforcement Learning Algorithm for 2-DOF Helicopter Model
Andrew Fandel, Anthony Birge, Suruz Miah · 2018
This paper examines a reinforcement learning strategy for controlling a two degree-of-freedom (2-DOF) helicopter. The pitch and yaw angles are regulated to their corresponding reference angles by applying appropriate actuator commands (input voltages) to the main and tail rotors of a 2-DOF helicopter using the proposed reinforcement learning [herein called the approximate dynamic programming (ADP)] strategy. Furthermore, the proposed strategy has the ability to configure the 2-DOF helicopter to track time-varying reference angles. The proposed ADP technique is capable of dealing with coupling effects between the rigid body structure and propeller dynamics associated with the 2-DOF helicopter model considered in this work. A set of computer simulations is conducted to evaluate the performance of the proposed algorithm. The performance of the proposed algorithm is also compared to that of a conventional linear-quadratic regulator (LQR).