A Novel Optimization Framework for High Dynamics Point-to-Point Direct Drive Motion Control System With a New Type of Surrogate Model

This is the authors' abstract. We don't add key points for this paper.

A new optimization framework for a high-dynamic point-to-point direct drive motion control system (HDPDMS) is proposed. The conventional system optimization approach considers all design parameters simultaneously, resulting in a high-dimensional search space and extensive computation. In contrast, the proposed framework uses a new DDM surrogate model that establishes a correlation between the key DDM characteristic parameters to decouple the whole optimization process. It begins with a system-level optimization to identify suitable driver types, motion profile design parameters, and characteristic parameters of the direct drive motors (DDMs) by the new surrogate model. Bayesian optimization then determines the DDM design parameters corresponding to the identified characteristic parameters. Once the DDM surrogate model is built, the proposed framework achieved the desired HDPDMS design in just 1 hour, saving 98.6% of computation time compared to the traditional approach. Additionally, multi-objective optimization and Gaussian process regression prediction intervals were employed to obtain a suitable training dataset and input range for the surrogate model, resulting in a 99.8% reduction in computation resources compared to the traditional DDM surrogate model. Through completing three unique motion task optimizations and creating a prototype, the optimization framework was proven effective, demonstrating the potential of this novel method.