Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)
This addresses control challenges for multirotor systems like TRAS, offering a model-free RL approach, but it is incremental as it applies an existing RL method to a specific domain.
The paper tackled controlling a Twin Rotor Aerodynamic System (TRAS) using reinforcement learning, specifically the TD3 algorithm, to stabilize it at specific angles and track trajectories, showing effectiveness in simulations and real-world experiments compared to PID controllers under wind disturbances.
This paper proposes a reinforcement learning (RL) framework for controlling and stabilizing the Twin Rotor Aerodynamic System (TRAS) at specific pitch and azimuth angles and tracking a given trajectory. The complex dynamics and non-linear characteristics of the TRAS make it challenging to control using traditional control algorithms. However, recent developments in RL have attracted interest due to their potential applications in the control of multirotors. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm was used in this paper to train the RL agent. This algorithm is used for environments with continuous state and action spaces, similar to the TRAS, as it does not require a model of the system. The simulation results illustrated the effectiveness of the RL control method. Next, external disturbances in the form of wind disturbances were used to test the controller's effectiveness compared to conventional PID controllers. Lastly, experiments on a laboratory setup were carried out to confirm the controller's effectiveness in real-world applications.