High-Precision Trajectory Generation Based on Data-Driven Model Predictive Control With Weight Optimization

This paper presents a comprehensive framework for trajectory generation based on data-driven model predictive control (MPC) for industrial applications. The proposed method effectively combines a transfer function model, which is identified from operational data, with a neural network to address and mitigate modeling errors. In contrast to conventional online MPC approaches, the method formulated in this paper treats trajectory generation as an offline optimization problem, wherein the position sequence is directly optimized. Multiple performance metrics are jointly optimized, with the cost function weights being automatically tuned through machine learning-based optimization techniques, all while adhering to explicit tracking error constraints. Experimental validation conducted on a linear guide system demonstrates that the proposed method achieves both high tracking performance and smooth, practical trajectories, eliminating the need for manual parameter adjustments. This framework offers a robust and adaptable solution for advanced trajectory design applicable across a variety of industrial applications.