Analysis of Coupling Mechanism and Research on Control Strategy for Rotorcraft Aerial Manipulators

Recently, automation using Aerial Manipulators (AM) plays a vital role in the consumer centric industries where tasks need to be accurate with stability. Rotorcraft aerial manipulators are hybrid robotic systems composed of multi-rotor unmanned aerial vehicles (UAVs) and robotic arms. They possess advantages such as high-altitude flexible maneuverability and interactive operation, rendering significant application value in fields like high-altitude maintenance, hazardous material transportation, and emergency rescue. However, the strong nonlinear force-position coupling between the UAV and the robotic arm during operation leads to unstable flight attitudes and increased positioning errors at the end-effector of the robotic arm. Traditional PID control methods struggle to cope with such complex coupling characteristics. To address this critical issue, this paper first reveals the chain coupling mechanism of "centroid offset-inertial transfer-aerodynamic disturbance" for rotorcraft aerial manipulators from three dimensions combined with dynamic simulations. On this basis, a multi-field coupling dynamic equation covering centroid offset, inertial coupling, and aerodynamic disturbance is innovatively established, providing a theoretical foundation for the design of control strategies. Furthermore, an adaptive PID control strategy based on multi-agent reinforcement learning (MAAC-PID) is proposed, which innovatively adopts a "centralized learning-distributed execution" architecture. Through two agents optimizing the UAV's PID parameters and the robotic arm's joint control respectively, global cooperative optimization of the coupled system is achieved. Simulation results show the MAAC-PID algorithm outperforms traditional PID and PPO, reducing trajectory errors and enhancing stability. Specifically, under 2KG load condition, the standard deviation of the error of the MAAC-PID algorithm in the square trajectory is reduced by approximately 16.1% compared with the PPO algorithm; in addition, the MAAC-PID algorithm achieves an improvement of 0.03m in average maximum trajectory error compared with the PPO algorithm, exhibiting stronger robustness and adaptability.