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Parameter identification of the phenomenological model for magnetorheological fluid dampers using hierarchic enhanced particle swarm optimization

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Abstract

The magnetorheological (MR) fluid damper, as a new generation of high-performance and intelligent vibration damping devices for engineering structures, has broad application prospects in structural vibration reduction. Nonlinear hysteresis is a complex phenomenon of MR dampers. In this paper, the phenomenological model is used to simulate the dynamics of MR dampers. To accurately and efficiently identify the parameters of the phenomenological model, the hierarchical enhanced particle swarm optimization (HEPSO) algorithm is proposed. This algorithm applies a hierarchical optimization mechanism and introduces media particles to the enhanced PSO (EPSO) algorithm to improve the optimization process of parameter identification and enhance the performance of the PSO algorithm without reducing the possibility of finding the optimal solution. Compared with the standard PSO (SPSO) algorithm and the EPSO algorithm, the HEPSO algorithm has a higher efficiency, accuracy and robustness in identifying a highly nonlinear hysteretic system problem.

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Abbreviations

f :

Damping force

x :

Relative displacement

\(\bar{x}\) :

Velocity of the piston

y :

Relative displacement of the damping element with damping coefficient

\(\bar{y}\) :

Velocity of the damping element with damping coefficient c1

Z :

Evolutionary variable

k 1 :

The stiffness of the accumulator

k 0 :

The stiffness of the MR damper at large velocities

c 0 :

The viscous damping of the MR damper at larger velocities

\(c_{1}^{d}\) :

The viscous damping of the MR damper at low velocities

x 0 :

The initial displacement of spring k1

u :

Effective voltage

p :

The parameter vector of the MR damper

N :

The number of data points

\(\sigma_{f}^{2}\) :

The variance of the experimental data of the damping force

N p :

Size of the population

\(x_{k}^{i}\) :

Position vector

\(p_{k}^{i}\) :

Historical optimal position vector

\(v_{k}^{i}\) :

Velocity vector

\(v_{0}^{max}\) :

The initial maximum velocity of the individuals

ρ :

The maximum velocity proportion factor

w :

Inertia factor

a :

The inertial factor reduction coefficient b

r :

A random variable with a uniform distribution in the interval [0, 1]

c 3 :

The smart velocity factor

λ :

The reduction factor determining the next level search space

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Acknowledgments

This work is supported financially by the Natural Science Foundation of China (Project No. 51508351, No. 51508350), the Natural Science Foundation of Hebei Province (No. E2017210117) and the Education Department of Hebei Province (No. ZD2017071).

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Jin Guo & Mengxuan Zhang

  2. Structural Health Monitoring and Control Institute, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Zhendong Li

Authors
  1. Jin Guo
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Corresponding author

Correspondence to Zhendong Li.

Additional information

Recommended by Editor No-cheol Park

Jin Guo is an Associate Professor of the Department of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, China. He received his Ph.D. in civil engineering from Tongji University. His research interests include bridge seismic, hybrid test methods and bridge health monitoring. He has published more than 10 papers in domestic and foreign journals.

Zhendong Li is a master student of the Structural Health Monitoring and Control Institute, Shijiazhuang Tiedao University, Shijiazhuang, China. His major is safety science and engineering. His research interests include bridge seismic and bridge health monitoring.

Mengxuan Zhang is a master student of the Department of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, China. His major is bridge and tunnel engineering. His research interests include bridge seismic and bridge health monitoring.

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Guo, J., Li, Z. & Zhang, M. Parameter identification of the phenomenological model for magnetorheological fluid dampers using hierarchic enhanced particle swarm optimization. J Mech Sci Technol 35, 875–887 (2021). https://doi.org/10.1007/s12206-021-0202-3

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  • DOI: https://doi.org/10.1007/s12206-021-0202-3

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