A hybrid clustering based fuzzy structure for vibration control - Part 2: An application to semi-active vehicle seat-suspension system
作者： Sy Dzung Nguyen , Quoc Hung Nguyen , Seung-Bok Choi , None
DOI: 10.1016/J.YMSSP.2014.10.019
关键词:
摘要: Abstract This work presents a novel neuro-fuzzy controller (NFC) for car-driver׳s seat-suspension system featuring magnetorheological (MR) dampers. The NFC is built based on the algorithm building adaptive inference systems (ANFISs) named B-ANFIS, which has been developed in Part 1, and fuzzy logic (FISs). In order to create NFC, following steps are performed. Firstly, control strategy ride-comfort-oriented tendency (RCOT) established. Subsequently, optimal FISs genetic (GA) estimate desired damping force that satisfies RCOT corresponding road status at each time. B-ANFIS then used build ANFISs inverse dynamic models of suspension (I-ANFIS). Based FISs, values calculated according exciting current value be applied MR damper determined by I-ANFIS. validate effectiveness controller, performances dampers evaluated under different conditions. addition, comparative between conventional skyhook proposed undertaken demonstrate superior methodology.
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sci-hub.se 参考文章(14)
Bogdan Sapiński, Fuzzy control for MR damper in a driver's seat suspension Journal of Theoretical and Applied Mechanics. ,vol. 43, pp. 179- 201 ,(2005)
Tomasz Krzyżyński, Igor Maciejewski, CONTROL DESIGN OF SEMI-ACTIVE SEAT SUSPENSION SYSTEMS Journal of Theoretical and Applied Mechanics. ,vol. 49, pp. 1151- 1168 ,(2011)
Haiping Du, Nong Zhang, Static Output Feedback Control for Electrohydraulic Active Suspensions via T–S Fuzzy Model Approach Journal of Dynamic Systems Measurement and Control-transactions of The Asme. ,vol. 131, pp. 051004- ,(2009) , 10.1115/1.3117194
Sy Dzung Nguyen, Seung-Bok Choi, None, A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers Smart Materials and Structures. ,vol. 21, pp. 085021- ,(2012) , 10.1088/0964-1726/21/8/085021
Lu-Hang Zong, Xing-Long Gong, Chao-Yang Guo, Shou-Hu Xuan, Inverse neuro-fuzzy MR damper model and its application in vibration control of vehicle suspension system Vehicle System Dynamics. ,vol. 50, pp. 1025- 1041 ,(2012) , 10.1080/00423114.2011.645489
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
I. Ballo, Comparison of the properties of active and semiactive suspension Vehicle System Dynamics. ,vol. 45, pp. 1065- 1073 ,(2007) , 10.1080/00423110701191575
Mohammad Biglarbegian, William Melek, Farid Golnaraghi, A novel neuro-fuzzy controller to enhance the performance of vehicle semi-active suspension systems Vehicle System Dynamics. ,vol. 46, pp. 691- 711 ,(2008) , 10.1080/00423110701585420
Chaochao Chen, Bin Zhang, G. Vachtsevanos, M. Orchard, Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering IEEE Transactions on Industrial Electronics. ,vol. 58, pp. 4353- 4364 ,(2011) , 10.1109/TIE.2010.2098369
AA Aldair, WJ Wang, A neurofuzzy controller for full vehicle active suspension systems Journal of Vibration and Control. ,vol. 18, pp. 1837- 1854 ,(2012) , 10.1177/1077546311428631