Optimization of Adaptive Algorithm for Precise Motion Control of Multi-Degree-of-Freedom Robotic Arms

With the rapid development of industrial automation and smart manufacturing, multi-degree-of-freedom robotic arms face the severe challenge of high-precision motion control in dynamic and complex environments. To overcome the limitations of traditional adaptive algorithms in terms of adaptability and computational efficiency, this study proposes an optimization strategy framework integrating multimodal perception and deep reinforcement learning. By leveraging a collaborative perception network of vision, tactile sensing, and IMU, we collected dynamic data in real time and built a dynamic prediction model driven by deep reinforcement learning. Through transfer learning, we achieved cross-scenario generalization capabilities and relied on an edge computing platform for millisecond-level real-time response. Experimental validation showed that the accuracy of dynamic disturbance suppression improved by $42.1 \%$, task execution cycle was reduced by $31.5 \%$, achieving positioning accuracy of $\pm 0.1 \mathrm{~mm}$, and the operational success rate in unstructured environments reached $99.3 \%$. Compared to traditional PID and fuzzy control, the overshoot in force control was reduced to $2.7 \%$, and the adaptation time for stiffness mutation was optimized by $\mathbf{4 6 \%}$. This research successfully broke through the bottleneck of precise control in dynamically unstable environments, providing a highly adaptable solution strategy for intelligent manufacturing equipment.