Novel Gaussian Acceleration Profile for Smooth Jerk-Bounded Trajectories

Abstract

Industrial machines regularly work at their limits causing excessive long-term vibrations that deteriorate their movement, stability, and precision. In this sense, reference profiles are able to reduce the detrimental vibration effects by manipulating the machine motion dynamics using predefined movement trajectories. Most of the approaches for lessening damages due to long-term machine vibrations are based on polynomial functions with high computational complexity and high resources demand. Hence, in this work, an innovative acceleration outline based on a Gaussian function is proposed for machine motion trajectories. The introduced strategy simplifies the position-profile estimation that works as reference for restraining the machine movements through the parameters that define its motion dynamics; thus, a smooth and continuous jerk contour is produced, which reduces vibrations and improves the machine stability. Exhaustive computer-based and real-time experimental comparisons of the introduced scheme produces a significantly lower maximum jerk value than any of the others. The assessment of the presented approach was performed utilizing the software Matlab (R2020a) on a PC with an Intel Core i7-6500U microprocessor at 2.5 GHz, with 16 GB in RAM and a 64-bit operating system.

Keywords

• Acceleration profile
• Gaussian function
• motion dynamics
• smooth jerk trajectory
• vibration reduction