Evaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots
Damir Nurtdinov, Aliaksei Korshuk, Alexei Kornaev, Alexander Maloletov
TL;DR
This work tackles robust control of underconstrained Cable-Driven Parallel Robots (CDPRs) under limited time discretization ($Δt$). It compares a classical PD controller with reinforcement learning policies—DDPG, PPO, and TRPO—using a custom OpenAI Gym CDPR environment and a kinematic/dynamics model including $l_i = c - a_i + R \times b_i$ with a Jacobian $J = [S_1, S_2, S_3, S_4]^T$. Across three trajectories, TRPO achieves the smallest RMS error and remains robust at larger $Δt$, outperforming DDPG, PPO, and the PD controller by effectively balancing exploration and exploitation in noisy, real-world settings. The findings position TRPO as a practical, robust controller for dynamic CDPR tasks and motivate future physical experiments and integration with sensor fusion or hybrid control strategies.
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), 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.