用强化学习规划轨迹，结合微分方程控制，有效抑制柔性机械臂振动。
Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators
- 用DRL算法生成低振动的运动轨迹
- 实测与仿真均实现更高跟踪精度和振动抑制效果
- 适合需要高精度控制的柔性机械臂系统
本文提出一种融合深度强化学习（DRL）与非线性偏微分方程（PDE）控制器的柔性机械臂运动规划与控制框架。不同于传统仅关注控制的方法，研究发现期望轨迹对末端振动有显著影响。为此，采用软演员-评论家（SAC）算法训练DRL运动规划器，生成天然抑制振动的优化轨迹。PDE非线性控制器基于该轨迹计算所需力矩，利用李雅普诺夫分析确保闭环稳定性。所提方法通过仿真与真实实验验证，相比传统方法在振动抑制和轨迹跟踪精度上表现更优。结果表明，将学习型运动规划与模型化控制结合，能显著提升柔性机械臂的精度与稳定性。
原文摘要 · Abstract (English)
This article presents a motion planning and control framework for flexible robotic manipulators, integrating deep reinforcement learning (DRL) with a nonlinear partial differential equation (PDE) controller. Unlike conventional approaches that focus solely on control, we demonstrate that the desired trajectory significantly influences endpoint vibrations. To address this, a DRL motion planner, trained using the soft actor-critic (SAC) algorithm, generates optimized trajectories that inherently minimize vibrations. The PDE nonlinear controller then computes the required torques to track the planned trajectory while ensuring closed-loop stability using Lyapunov analysis. The proposed methodology is validated through both simulations and real-world experiments, demonstrating superior vibration suppression and tracking accuracy compared to traditional methods. The results underscore the potential of combining learning-based motion planning with model-based control for enhancing the precision and stability of flexible robotic manipulators.
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