TY - JOUR
T1 - Enhanced representation of the nonlinear dynamic characteristics of ball screw feed drive system through developing a three-state model
AU - Wan, Min
AU - Dai, Jia
AU - Tian, Hui
AU - Qin, Xue Bin
AU - Zhang, Wei Hong
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/3/15
Y1 - 2025/3/15
N2 - This paper develops a three-state model to represent the nonlinear dynamic characteristics of the ball screw feed drive system (BSFDS), including the impact of backlash during velocity reversal. The three states are distinguished as a rigid model during unidirectional motion, a rigid model capturing the backlash phenomenon during velocity reversal, and a flexible model capable of describing the torsional vibrations. Impact of backlash on friction and inertia is considered in the three-state model, with a special friction model established to depict the effect of backlash. To identify the parameters involved in the model, a recursive maximum likelihood (RML) algorithm is proposed, based on the distinct signals designed specially for exciting the three states. The accuracy of parameter identification is enhanced through the combined use of iterative learning (IL). Based on the identified friction results using the IL-based RML, the model parameters are finally determined through least squares fitting. Experiments and comparisons are conducted to verify the effectiveness of this work.
AB - This paper develops a three-state model to represent the nonlinear dynamic characteristics of the ball screw feed drive system (BSFDS), including the impact of backlash during velocity reversal. The three states are distinguished as a rigid model during unidirectional motion, a rigid model capturing the backlash phenomenon during velocity reversal, and a flexible model capable of describing the torsional vibrations. Impact of backlash on friction and inertia is considered in the three-state model, with a special friction model established to depict the effect of backlash. To identify the parameters involved in the model, a recursive maximum likelihood (RML) algorithm is proposed, based on the distinct signals designed specially for exciting the three states. The accuracy of parameter identification is enhanced through the combined use of iterative learning (IL). Based on the identified friction results using the IL-based RML, the model parameters are finally determined through least squares fitting. Experiments and comparisons are conducted to verify the effectiveness of this work.
KW - Ball screw feed drive
KW - Dynamic characteristics
KW - Flexible system
KW - Parameter identification
UR - http://www.scopus.com/inward/record.url?scp=85216118812&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2025.112371
DO - 10.1016/j.ymssp.2025.112371
M3 - 文章
AN - SCOPUS:85216118812
SN - 0888-3270
VL - 227
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
M1 - 112371
ER -