This study aims to improve the dynamic performance of multidimensional force sensors used in cutting force measurements. Conventional sensors often suffer from low intrinsic frequencies, poor damping ratios and limited bandwidths, which restrict their ability to capture dynamic forces accurately.
A dynamic compensation method based on the improved red-tailed hawk algorithm (IRTHA) is proposed. The algorithm uses sine–tent–cosine chaotic mapping to enhance population diversity and introduces a cosine-based transition factor with dynamic weights to balance exploration and exploitation. Enhanced gravity factors and hybrid perturbation strategies further strengthen global and local optimization. The method is validated through benchmark functions and dynamic calibration experiments of a 3D force sensor.
Results show that the IRTHA-based compensator significantly reduces overshoot and regulation time, improving both the dynamic response and measurement accuracy of the sensor.
This work presents a novel dynamic compensation framework integrating advanced chaotic mapping and adaptive search mechanisms. The proposed method offers superior optimization capability and provides an effective solution for enhancing the dynamic measurement performance of multidimensional force sensors.
