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Semantic Web-Driven Hybrid Adaptive Control System for Trajectory Tracking in Robotic Arms
Abstract
In modern industrial production, trajectory tracking control of multi-degree-of-freedom robotic arms entails safety and efficiency. This study focuses on a two-degree-of-freedom manipulator, developing a mechanical dynamics model and implementing a parallel Linear Active Disturbance Rejection Controller (LADRC) for joint control. To enhance robustness and responsiveness, a hybrid adaptive control method is introduced, integrating adaptive laws (AL) for online tuning of parameters and offline optimization using the Deep Deterministic Policy Gradient (DDPG) algorithm. This improves tracking control efficiency and effectiveness. Additionally, offline reinforcement learning refines parameters without adding real-time computational demands. Finally, semantic web principles ensure interpretability and transparency of the control flow. Compared to traditional LADRC, the proposed method shows superior anti-interference capabilities and dynamic performance.
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