This study focuses on error analysis and adaptive compensation in robot trajectory tracking control, proposing a datadriven adaptive control solution. The study first systematically identifies the sources of static and dynamic errors in trajectory tracking and constructs a multi-dimensional error model. Then, a complete data-driven control framework is established, including data acquisition and preprocessing, machine learning control modeling, and adaptive algorithm selection. An adaptive compensation mechanism integrating feedforward and feedback is designed, and a quantitative evaluation system based on tracking accuracy, stability, and response speed is formulated. Finally, the effectiveness of the method is verified through various robot experimental scenarios, and the performance of different control algorithms is compared and analyzed. Experimental results show that the proposed data-driven adaptive compensation method can reduce the average trajectory tracking error by 23.7%, shorten the response time by 18.3%, and improve system stability to over 95%. Compared with traditional PID and MPC methods, it achieves significant improvements in anti-interference capability and tracking accuracy. This research provides a new data-driven method for solving the error problem of robot trajectory tracking under complex working conditions, effectively improving the adaptability and robustness of the robot control system, and providing theoretical reference and technical support for high-precision trajectory tracking control in fields such as unmanned driving, industrial robots, and special-operation robots.

trajectory tracking control, robotics, adaptive compensation, data driven, high-precision control