Evaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots

Nurtdinov Damir, Aliaksei Korshuk, Alexei Kornaev, Alexander Maloletov

Keywords: Reinforcement Learning, DDPG, PPO, TRPO, PID, control, cable-driven robot

TL;DR: TRPO outperforms PID and other RL algorithms in controlling Cable-Driven Parallel Robots, achieving lower RMS errors and robust performance with larger time intervals, ideal for real-world applications with limited sensor feedback.

Abstract: 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), Soft Actor-Critic (SAC), 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.

Submission Number: 18