Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Qi Liu, Hong Lu (), Heisei Yonezawa, Ansei Yonezawa, Itsuro Kajiwara and Ben Wang
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Qi Liu: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Hong Lu: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Heisei Yonezawa: Division of Mechanical and Aerospace Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo 060-8628, Hokkaido, Japan
Ansei Yonezawa: Division of Mechanical and Aerospace Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo 060-8628, Hokkaido, Japan
Itsuro Kajiwara: Division of Mechanical and Aerospace Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo 060-8628, Hokkaido, Japan
Ben Wang: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2023, vol. 11, issue 10, 1-29

Abstract: Dual-ball-screw feed drive systems (DBSFDSs) are designed for most high-end manufacturing equipment. However, the mismatch between the dynamic characteristic parameters (e.g., stiffness and inertia) and the P-PI cascade control method reduces the accuracy of the DBSFDSs owing to the structural characteristic changes in the motion. Moreover, the parameters of the P-PI cascade controller of the DBSFDSs are always the same even though the two axes have different dynamic characteristics, and it is difficult to tune two-axis parameters simultaneously. A new application of the combination of the grey wolf optimization (GWO) algorithm and the P-PI cascade controller is presented to solve these problems and enhance the motion performance of DBSFDSs. The novelty is that the flexible coupling model and dynamic stiffness obtained from the motor current can better represent the two-axis coupling dynamic characteristics, and the GWO algorithm is used to adjust the P-PI controller parameters to address variations in the positions of the moving parts and reflect characteristic differences between the two axes. Comparison of simulation and experimental results validated the superiority of the proposed controller over existing ones in practical applications, showing a decrease in the tracking error of the tool center and non-synchronization error of over 34% and 39%, respectively.

Keywords: grey wolf optimization algorithm; P-PI cascade controller; dual-ball-screw feed drive system; dynamic characteristic parameter; characteristic variation; flexible coupling model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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