Vibration Suppression for Large-Scale Flexible Structures Using Deep Reinforcement Learning Based on Cable-Driven Parallel Robots
Vibration Suppression for Large-Scale Flexible Structures Using Deep Reinforcement Learning Based on Cable-Driven Parallel Robots
复制标题
基于电缆驱动并联机器人的深度强化学习大规模柔性结构振动抑制
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Wei Jinhao
中科院分区：
文献类型：
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作者：
Haining Sun;Xiaoqiang Tang;Wei Jinhao
具有超长翼的特殊卫星在许多领域发挥着至关重要的作用。然而，外部扰动和自旋会导致柔性翼产生不期望的振动，影响卫星的正常运行。在严重的情况下，卫星将被损坏。因此，对这些柔性结构进行减振势在必行。利用深度强化学习（DRL），提出了一种基于索驱动并联机器人（CDPR）的主动控制方案，以较小的可控力快速抑制柔性结构的振动。为了验证控制器的有效性，进行了三组不同初始扰动的仿真。此外，为了增强对比，还试验了一种被动预紧方案。首先，采用有限元法建立了由四根索和柔性结构组成的CDPR的动力学模型。然后，用Newmark-ß法分析了模型在可控索力作用下的动力特性。在此基础上，采用深度确定性策略梯度算法（deep deterministic policy gradient algorithm， DDPG）训练DRL智能体。最后，在Simulink环境下对该控制方案进行了性能评估，结果令人满意，验证了该控制器抑制振动的能力。
Specific satellites with ultra-long wings play a crucial role in many fields. However, external disturbance and self-rotation could result in undesired vibrations of flexible wings, which affects the normal operation of the satellites. In severe cases, the satellites will be damaged. Therefore, it is imperative to conduct vibration suppression for these flexible structures. Utilizing deep reinforcement learning (DRL), an active control scheme is presented in this paper to rapidly suppress the vibration of flexible structures with quite small controllable force based on a cable-driven parallel robot (CDPR). To verify the controller’s effectiveness, three groups of simulation with different initial disturbance are implemented. Besides, to enhance the contrast, a passive pre-tightening scheme is also tested. First, the dynamic model of the CDPR that is comprised of four cables and a flexible structure is established using the finite element method. Then, the dynamic behavior of the model under the controllable cable force is analyzed by Newmark-ß method. Furthermore, the agent of DRL is trained by the deep deterministic policy gradient algorithm (DDPG). Finally, the control scheme is conducted on Simulink environment to evaluate its performance, and the results are satisfactory, which validates the controller’s ability to suppress vibrations.