Reinforcement learning-based controller design for a proposed octorotor with tilt-arm angles
The maneuverability of a quadrotor or octorotor UAV is limited in the standard configuration because the force vectors of the propellers are parallel and only have four active degrees of freedom. Therefore, they lack the controllability of six independent degrees of freedom. This study designs a novel configuration for an octorotor capable of hovering with roll or pitch angles in a specific position, contrary to UAVs with a standard configuration that can only hover in a horizontal position. In other words, in this octorotor, orientation tracking is also added to the octorotor's targets in addition to position tracking. The proposed model can be controlled by altering the velocity of the eight rotors and the tilt angle of the four arms. Such alterations in velocity and tilt angle are such that they can provide the aerial vehicle with most optimum maneuverability. After deriving the proposed dynamic octorotor model, a controller is proposed using neural networks (NNs) and reinforcement learning (RL), capable of controlling the proposed octorotor with six independent degrees of freedom. Finally, trajectory tracking, octorotor position, and controller robustness to possible motor malfunctions are examined, and numerical simulation results are provided.
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