Nonparametric Modeling of an Automotive Damper Based on ANN: Effect in the Control of a Semi-active Suspension
作者： Juan C. Tudón-Martínez , Ruben Morales-Menendez
DOI: 10.1007/978-3-319-11271-8_19
关键词: Parametric model 、 Suspension (motorcycle) 、 Control theory 、 Mean squared error 、 Nonparametric statistics 、 Control theory 、 Control system 、 Artificial neural network 、 Damper 、 Control engineering 、 Engineering
摘要: A model for a Magneto-Rheological (MR) damper based on Artificial Neural Networks (ANN) is proposed. The design of the ANN focused to get best architecture that manages trade-off between computing cost and performance. Experimental data provided from two MR dampers with different properties have been used validate performance proposed in comparison classical parametric Bingham. Based RMSE index, an average error 7.2 % obtained by model, taking into account 5 experiments 10 replicas each one; while Bingham has 13.8 error. Both structures were suspension control system Quarter Vehicle (QoV) order evaluate effect its accuracy design/evaluation system. Simulation results show accurate ANN-based fulfills goals; does not fulfill them, concluding erroneously controller insufficient must be redesigned. validates realistic QoV response compliance.
springer.com
sci-hub.st 参考文章(28)
Jonas Sjöberg, Non-Linear System Identification with Neural Networks Linköping University. ,(1995)
Jzef Korbicz, W. Cholewa, Jozef Karbicz, J. M. Koscielny, Z. Kowalczuk, Fault Diagnosis: Models, Artificial Intelligence, Applications Springer. ,(2004)
Xiao-min Dong, Miao Yu, Chang-rong Liao, Wei-min Chen, Comparative research on semi-active control strategies for magneto-rheological suspension Nonlinear Dynamics. ,vol. 59, pp. 433- 453 ,(2010) , 10.1007/S11071-009-9550-8
Cristiano Spelta, Sergio M. Savaresi, Luca Fabbri, Experimental analysis of a motorcycle semi-active rear suspension Control Engineering Practice. ,vol. 18, pp. 1239- 1250 ,(2010) , 10.1016/J.CONENGPRAC.2010.02.006
Haiping Du, James Lam, Nong Zhang, Modelling of a magneto-rheological damper by evolving radial basis function networks Engineering Applications of Artificial Intelligence. ,vol. 19, pp. 869- 881 ,(2006) , 10.1016/J.ENGAPPAI.2006.02.005
Juan C. Tudón-Martínez, Ruben Morales-Menendez, Ricardo Ramírez-Mendoza, Olivier Sename, Luc Dugard, Comparison between a Model-free and Model-based Controller of an Automotive Semi-active Suspension System * * IFAC Proceedings Volumes. ,vol. 46, pp. 869- 874 ,(2013) , 10.3182/20130204-3-FR-2033.00039
H. Metered, P. Bonello, S.O. Oyadiji, The experimental identification of magnetorheological dampers and evaluation of their controllers Mechanical Systems and Signal Processing. ,vol. 24, pp. 976- 994 ,(2010) , 10.1016/J.YMSSP.2009.09.005
R. Stanway, J.L. Sproston, N.G. Stevens, Non-linear modelling of an electro-rheological vibration damper Journal of Electrostatics. ,vol. 20, pp. 167- 184 ,(1987) , 10.1016/0304-3886(87)90056-8
Haiping Du, Kam Yim Sze, James Lam, Semi-active H∞ control of vehicle suspension with magneto-rheological dampers Journal of Sound and Vibration. ,vol. 283, pp. 981- 996 ,(2005) , 10.1016/J.JSV.2004.05.030
NM Kwok, QP Ha, TH Nguyen, Jianchun Li, Bijan Samali, None, A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization Sensors and Actuators A: Physical. ,vol. 132, pp. 441- 451 ,(2006) , 10.1016/J.SNA.2006.03.015