TRPO算法在真实电缆机器人中表现最优，抗干扰强且降低对高频传感依赖。
Evaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots
- 用TRPO替代传统PID和DDPG/PPO，优化控制策略
- TRPO在多种轨迹下误差最低，最大控制间隔达100ms仍稳定
- 适合高噪声、低频传感环境的复杂机器人控制场景
本研究评估了经典与现代控制方法在真实世界电缆驱动并联机器人（CDPRs）中的表现，重点关注受限自由度且时间离散化有限的系统。对比分析了经典PID控制器与深度确定性策略梯度（DDPG）、近端策略优化（PPO）及信任区域策略优化（TRPO）等强化学习算法。结果表明，TRPO在各类轨迹上均实现最低的均方根（RMS）误差，并在较大控制更新间隔（最高100ms）下保持鲁棒性。其在探索与利用间的良好平衡，使系统在噪声环境中仍能稳定运行，减少对高频传感器反馈和高算力的需求。研究揭示了TRPO在复杂机器人控制任务中的潜力，为动态环境下的传感器融合或混合控制策略提供新思路。
原文摘要 · Abstract (English)
This study evaluates the performance of classical and modern control methods for real-world Cable-Driven Parallel Robots (CDPRs), focusing on underconstrained systems with limited time discretization. A comparative analysis is conducted between classical PID controllers and modern reinforcement learning algorithms, including Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO). The results demonstrate that TRPO outperforms other methods, achieving the lowest root mean square (RMS) errors across various trajectories and exhibiting robustness to larger time intervals between control updates. TRPO's ability to balance exploration and exploitation enables stable control in noisy, real-world environments, reducing reliance on high-frequency sensor feedback and computational demands. These findings highlight TRPO's potential as a robust solution for complex robotic control tasks, with implications for dynamic environments and future applications in sensor fusion or hybrid control strategies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。