Frequency domain active vibration control of a flexible plate based on neural networks

doi:10.1007/s11465-013-0252-z

Front Mech Eng

2013, Vol. 8

Issue (2) : 109-117 https://doi.org/10.1007/s11465-013-0252-z

RESEARCH ARTICLE

Frequency domain active vibration control of a flexible plate based on neural networks

Jinxin LIU, Xuefeng CHEN(

), Zhengjia HE

Key State Laboratory of Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Download: PDF(554 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

A neural-network (NN)-based active control system was proposed to reduce the low frequency noise radiation of the simply supported flexible plate. Feedback control system was built, in which neural network controller (NNC) and neural network identifier (NNI) were applied. Multi-frequency control in frequency domain was achieved by simulation through the NN-based control systems. A pre-testing experiment of the control system on a real simply supported plate was conducted. The NN-based control algorithm was shown to perform effectively. These works lay a solid foundation for the active vibration control of mechanical structures.

Keywords active vibration control (AVC), neural network (NN), low frequency noise, frequency domain control multi-frequency control

Corresponding Author(s): CHEN Xuefeng,Email:Chenxf@mail.xjtu.edu.cn

Issue Date: 05 June 2013

Cite this article:

Jinxin LIU,Xuefeng CHEN,Zhengjia HE. Frequency domain active vibration control of a flexible plate based on neural networks[J]. Front Mech Eng, 2013, 8(2): 109-117.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-013-0252-z
https://academic.hep.com.cn/fme/EN/Y2013/V8/I2/109

Fig.1 Finite element model of the simply supported plate. (a) Element division of the plate under the global coordinate system ; (b) DOFs of the th element under the local coordinate system

Fig.2 Schematic diagram of the system. NNC: neural network controller; NNI: neural network identifier; F→T: frequency domain to time domain; T→F: time domain to frequency domain

Fig.3 Architecture of neural network controller

Fig.4 Architecture of neural network identifier

Fig.5 Flow diagram of the control system

Fig.6 Simulation result when = 0.1. (a) Target spectrum; (b) controlled spectrum; (c) error curve; (d) time domain amplitude of 10 Hz component; (e) time domain amplitude of 20 Hz component

Fig.7 Simulation result when = 0.01. (a) Target spectrum; (b) controlled spectrum; (c) error curve; (d) time domain amplitude of 10 Hz component; (e) time domain amplitude of 20 Hz component

Fig.8 Structure of the experiment system (notes: IPC stands for industrial personal computer; RVB stands for red vs blue; USB stands for universal serial bus; BNC stands for bayonet nut connector; VHDCI stands for very high density cable interconnect; VGA stands for video graphic array)

Fig.9 Result of the pre-testing experiment. (a) Target spectrum; (b) controlled spectrum; (c) error curve; (d) target wave; (e) controlled wave

1	Hu Z W. A review of active vibration control for structures. Journal of Mechanical Strength , 1995, 17(2): 55–60 (in Chinese)
2	Lane S A, Clark R L, Southward S C. Active control of low frequency modes in an aircraft fuselage using spatially weighted arrays. Journal of Vibration and Acoustics-Transactions of the ASME , 2000, 122(3): 227–234 doi: 10.1115/1.1303848
3	de Klerk D, Ossipov A. Operational transfer path analysis: theory, guidelines and tire noise application. Mechanical Systems and Signal Processing , 2010, 24(7): 1950–1962 doi: 10.1016/j.ymssp.2010.05.009
4	Alavinasab A, Moharrami H, Khajepour A. Active control of structures using energy-based LQR method. Computer-Aided Civil and Infrastructure Engineering , 2006, 21(8): 605–611 doi: 10.1111/j.1467-8667.2006.00460.x
5	Zhang J J, He L L, Wang E C, . The design of LQR controller based on independent mode space for active vibration control. Advances in Computation and Intelligence , 2008, 5370: 649–658
6	Gao H J, Sun W C, Shi P. Robust sampled-data H_∞ control for vehicle active suspension systems. IEEE Transactions on Control Systems Technology , 2010, 18(1): 238–245 doi: 10.1109/TCST.2009.2015653
7	Moheimani S, Vautier B, Bhikkaji B. Experimental implementation of extended multivariable PPF control on an active structure. IEEE Transactions on Control Systems Technology , 2006, 14(3): 443–455 doi: 10.1109/TCST.2006.872532
8	Rao V, Damle R, Tebbe C, Kern F. The adaptive control of smart structures using neural networks. Smart Materials and Structures , 1994, 3(3): 354–366 doi: 10.1088/0964-1726/3/3/011
9	Damle R, Lashlee R, Rao V, Kern F. Identification and robust control of smart structures using artificial neural networks. Smart Materials and Structures , 1994, 3(1): 35–46 doi: 10.1088/0964-1726/3/1/006
10	Damle R, Rao V, Kern F. Robust control of smart structures using neural network hardware. Smart Materials and Structures , 1997, 6(3): 301–314 doi: 10.1088/0964-1726/6/3/008
11	Jha R, He C L. Neural-network-based adaptive predictive control for vibration suppression of smart structures. Smart Materials and Structures , 2002, 11(6): 909–916 doi: 10.1088/0964-1726/11/6/312
12	Jnifene A, Andrews W. Experimental study on active vibration control of a single-link flexible manipulator using tools of fuzzy logic and neural networks. IEEE Transactions on Instrumentation and Measurement , 2005, 54(3): 1200–1208
13	Kumar R, Singh S P, Chandrawat H N. MIMO adaptive vibration control of smart structures with quickly varying parameters: neural networks vs classical control approach. Journal of Sound and Vibration , 2007, 307(3-5): 639–661 doi: 10.1016/j.jsv.2007.06.028
14	Madkour A, Hossain M A, Dahal K P, . Intelligent learning algorithms for active vibration control. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews , 2007, 37(5): 1022–1033
15	Abiyev R H, Kaynak O. Fuzzy wavelet neural networks for identification and control of dynamic plants-a novel structure and a comparative study. IEEE Transactions on Industrial Electronics , 2008, 55(8): 3133–3140 doi: 10.1109/TIE.2008.924018
16	Pan Y P, Wang J. Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks. IEEE Transactions on Industrial Electronics , 2012, 59(8): 3089–3101 doi: 10.1109/TIE.2011.2169636
17	Zhang X W, Chen X F, You S Q, He Z, Li B. Simulation and experimental investigation of structural dynamic frequency characteristics control. Sensors (Basel, Switzerland) , 2012, 12(4): 4986–5004 doi: 10.3390/s120404986 pmid:22666072

Viewed

Full text

Abstract

Cited

Shared

Discussed