In recent years, magnetorheological dampers (MRD) have played a significant role in vibration control in various fields such as vehicles, military, building structures, etc. However, the mechanical model of MRD is very complex due to its hysteresis, which makes it difficult to identify the model’s parameters. In addition to experimental data, under the condition of no other prior knowledge, eight unknown parameters of the Bouc-Wen model are identified in this study based on the particle swarm optimization (PSO) algorithm. According to the trend of parameter variation with current, fitting research is conducted, and the variation law of parameters with current is summarized. Based on the identified parameters, the MRD’s output force is predicted under any current other than the experimental current. Subsequently, the application of MRD in semi-active control of power equipment is performed out, and proposed Simulink calculation programs for single-stage and two-stage vibration control systems are built. Then, a comparative study with passive and active control is conducted. In this paper, linear quadratic regulator (LQR) control is adopted for the active control, and the controller’s parameters are optimized based on the PSO algorithm. The results show that the adopted semi-active control can significantly reduce the transmitted force from power equipment to the foundation and can effectively mitigate the disturbance of power equipment to the environment. This study offers important guidance for the vibration control of MRD in industrial engineering.
BalajiPSKarthik SelvaKumarK. Applications of nonlinearity in passive vibration control: a review. J Vib Eng Technol2021; 9: 183–213.
2.
LuZWangZZhouY, et al.Nonlinear dissipative devices in structural vibration control: a review. J Sound Vib2018; 423: 18–49.
3.
BeijenMAHeertjesMFButlerH, et al.Mixed feedback and feedforward control design for multi-axis vibration isolation systems. Mechatronics2019; 61: 106–116.
4.
ChristianJA. StarNAV: autonomous optical navigation of a spacecraft by the relativistic perturbation of starlight. Sensors2019; 19(19): 4064.
5.
AngKKWangSYQuekST. Weighted energy linear quadratic regulator vibration control of piezoelectric composite plates. Smart Mater Struct2002; 11(1): 98–106.
6.
ZhaoJZhenGLiuG, et al.Remarkable merits of triboelectric nanogenerator than electromagnetic generator for harvesting small-amplitude mechanical energy. Nano Energy2019; 61: 111–118.
7.
KarimiHRZapateiroMLuoN. Vibration control of base-isolated structures using mixed H2/H∞ output-feedback control. Proc IME J Syst Control Eng2009; 223(6): 809–820.
8.
LiuXLiSWuC, et al.Research on vibration control of power transmission lines-TMDI based on colliding bodies optimization. Buildings2022; 12(12): 2200.
9.
GucluRYaziciH. Vibration control of a structure with ATMD against earthquake using fuzzy logic controllers. J Sound Vib2008; 318(1): 36–49.
10.
SnyderSDTanakaN. Active control of vibration using a neural network. IEEE Transactions on1995; 6(4): 819–828.
11.
LinFJHwangWJWaiRJ. A supervisory fuzzy neural network control system for tracking periodic inputs [J]. Fuzzy Systems. IEEE Transactions on1999; 7(1): 41–52.
12.
TaghiradHDEsmailzadehE. Automobile passenger comfort assured through LQG/LQR active suspension. J Vib Control1998; 4(5): 603–618.
13.
KarINMiyakuraTSetoK. Bending and torsional vibration control of a flexible plate structure using H/sub ∞/-based robust control law. IEEE Transactions on2000; 8(3): 545–553.
14.
PourzeynaliSLavasaniHHModarayiAH. Active control of high rise building structures using fuzzy logic and genetic algorithms. Eng Struct2007; 29(3): 346–357.
15.
DongXYuMLiaoC, et al.Comparative research on semi-active control strategies for magneto-rheological suspension. Nonlinear Dynam2010; 59(3): 433–453.
16.
JaliliN. A comparative study and analysis of semi-active vibration-control systems. J Vib Acoust2002; 124(4): 593–605.
17.
LiHNLiZXQiA, et al.Structural vibration and control [M]. Beijing: China Construction Industry Press, 2005.
18.
LiuYMatsuhisaHUtsunoH. Semi-active vibration isolation system with variable stiffness and damping control. J Sound Vib2008; 313(1): 16–28.
19.
DuHLiWZhangN. Semi-active variable stiffness vibration control of vehicle seat suspension using an MR elastomer isolator. Smart Mater Struct2011; 20(10): 105003.
20.
LiuYWatersTPBrennanMJ. A comparison of semi-active damping control strategies for vibration isolation of harmonic disturbances. J Sound Vib2005; 280(1): 21–39.
21.
IbaDJrBFS. Vibration control using harmonically-varying damping. Journal of System Design and Dynamics2011; 5(5): 727–736.
22.
HirohataSIbaD. Frequency response analysis of multi-degree-of-freedom system with harmonically varying damping[C]//SPIE smart structures and Materials+ nondestructive evaluation and health monitoring. San Diego, California, USA: International Society for Optics and Photonics, 2012, p. 83453R.
23.
KamathGMWerelyNM. Nonlinear viscoelastic-plastic mechanisms-based model of an electrorheological damper. J Guid Control Dynam1997; 20(6): 1125–1132.
24.
SimsNDPeelDJStanwayR, et al.The electrorheological long-stroke damper: a new modelling technique with experimental validation. J Sound Vib2000; 229(2): 207–227.
25.
WangKWKimYSSheaDB. Structural vibration control via electrorheological-fluid-based actuators with adaptive viscous and frictional damping. J Sound Vib1994; 177(2): 227–237.
26.
ChoiSB. Vibration control of a flexible structure using ER dampers. J Dyn Syst Meas Control1999; 121(1): 134–138.
27.
YaoGZYapFFChenG, et al.MR damper and its application for semi-active control of vehicle suspension system. Mechatronics2002; 12(7): 963–973.
28.
LiWDongXYuJ, et al.Vibration control of vehicle suspension with magneto-rheological variable damping and inertia. J Intell Mater Syst Struct2021; 32(13): 1484–1503.
29.
ZhaoJLiKZhangXC, et al.Multidimensional vibration reduction control of the frame structure with magnetorheological damper. Struct Control Health Monit2020; 27(8): e2572.
30.
HusainSSMohammadRidhaT. Integral sliding mode control for seismic effect regulation on buildings using ATMD and MRD. Journale Européen des Systèmes Automatisés2022; 55(4): 541–548.
31.
RashidZTantrayMEhsanN, et al.Acceleration response-based adaptive strategy for vibration control and location optimization of magnetorheological dampers in multistoried structures. Pract Period Struct Des Construct2022; 27(1): 04021065.
32.
IkhouaneFRodellarJ. Systems with hysteresis: analysis, identification and control using the Bouc-Wen model[M]. Hoboken, NJ: John Wiley & Sons, 2007.
33.
SireteanuTGiucleaMMituAM. An analytical approach for approximation of experimental hysteretic loops by Bouc-Wen model. Proceedings of Romanian Academy, Series A, 2009, 10(1): 43-54.
34.
GiucleaMSireteanuTStancioiuD, et al.Model parameter identification for vehicle vibration control with magnetorheological dampers using computational intelligence methods. Proc Inst Mech Eng Part I: J Sys2004; 218(7): 569–581.
35.
KwokNMHaQPNguyenMT, et al.Bouc–Wen model parameter identification for a MR fluid damper using computationally efficient GA. ISA Trans2007; 46(2): 167–179.
36.
EberhartRCKennedyJ. A new optimizer using particle swarm theory[C]//Proceedings of the sixth international symposium on micro machine and human science. 1995, 1: 39-43.
37.
MarinakiMMarinakisYStavroulakisGE. Fuzzy control optimized by PSO for vibration suppression of beams. Control Eng Pract2010; 18(6): 618–629.
38.
AminiFHazavehNKRadAA. Wavelet PSO‐based LQR algorithm for optimal structural control using active tuned mass dampers. Computer aided Civil Eng2013; 28(7): 542–557.
39.
WenYK. Method for random vibration of hysteretic systems. J Eng Mech Div1976; 102(2): 249–263.
40.
YoshidaHKawataKFukuyamaY, et al.A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans Power Syst2000; 15(4): 1232–1239.
41.
GaingZL. Particle swarm optimization to solving the economic dispatch considering the generator constraints. IEEE Trans Power Syst2003; 18(3): 1187–1195.
NouiriMBekrarAJemaiA, et al.An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf2018; 29(3): 603–615.
44.
EberhartRCShiY. Comparing inertia weights and constriction factors in particle swarm optimization[C]. IEEE ASME J Microelectromech Syst2000; 1: 84–88.
45.
HuangTMohanAS. A hybrid boundary condition for robust particle swarm optimization. IEEE Antenn Wireless Propag Lett2005; 4: 112–117.
46.
RobinsonJRahmat-SamiiY. Particle swarm optimization in electromagnetics. IEEE Trans Antenn Propag2004; 52(2): 397–407.
47.
FarshidianfarASaghafiAKalamiSM, et al.Active vibration isolation of machinery and sensitive equipment using H∞ control criterion and particle swarm optimization method. Meccanica2012; 47: 437–453.
48.
ShiYEberhartR. A modified particle swarm optimizer[C]. In: 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360). Piscataway, NJ, January 15, 2013. IEEE, 1998, pp. 69–73.
49.
WeiYDLaiXBChenDZ, et al.Optimal parameters design of two-stage vibration isolation system. J Zhejiang Univ, 2006, 40(5): 893-896.
50.
LiuY. Semi-active damping control for vibration isolation of base disturbances [D]. Southampton: University of Southampton, 2004.
