用强化学习控制复杂直升机模型，抗干扰能力优于传统方法。
Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)
- 采用TD3算法实现连续动作空间的无模型控制
- 仿真与实测均验证其在风扰下稳定跟踪轨迹
- 适合多旋翼系统控制研究者参考
本文提出一种基于强化学习（RL）的框架，用于控制并稳定双旋翼气动系统（TRAS）在特定俯仰角和方位角下的运行状态，并实现给定轨迹的跟踪。由于TRAS具有复杂的非线性动力学特性，传统控制算法难以有效应对。近年来，强化学习因其在多旋翼控制中的潜力受到关注。本文采用双延迟深度确定性策略梯度（TD3）算法训练智能体，该算法适用于连续状态与动作空间，且无需系统模型。仿真结果表明该方法有效。随后，通过施加风扰测试控制器性能，对比传统PID控制器，验证其更强的鲁棒性。最后，在实验室平台进行实验，确认该控制器在真实场景中的可行性。
原文摘要 · Abstract (English)
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.
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