Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization

Razman, Mohd Azraai Mohd; Priyandoko, Gigih; Yusoff, Ahmad Razlan

doi:10.4028/www.scientific.net/amr.903.279

Cited by 19 publications

(11 citation statements)

References 17 publications

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“…Effective control of an MR damper mainly depends on understanding its nonlinear hysteretic behavior under an applied magnetic field. Therefore, the development of the distinct features and consider the non-linear behavior of the MR damper mechanism, this damper behavior was the extension of previous work [6]. The existing models can be categorized into two separate parametric and non-parametric groups.…”

Section: Introductionmentioning

confidence: 96%

Parametric Optimization of Magneto-Rheological Fluid Damper Using Particle Swarm Optimization

Azraai¹,

Priyandoko²,

Yusof³

et al. 2015

Int. J. Automot. Mech. Eng.

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This paper presents a parametric modeling of a magneto-rheological (MR) damper using a Particle Swarm Optimization (PSO) method. The objective of this paper is to optimize the parameter values of the MR fluid damper behavior using the Bouc-Wen model. The parametric identification was imposed beforehand in replicating the behavior of the MR fluid damper. The algebraic function from a number of hysteresis models was steered by comparing selected models: Bingham, Bouc-Wen and Bouc-Wen by Kwok. A simulation method was operated in investigating these models by employing MATLAB reliant from the model intricacy. The experimental data was presented in terms of the time histories of the displacement, the velocity and the force parameters, measured for both constant and variable current settings and at a selected frequency applied to the damper. The model parameters were determined using a set of experimental measurements corresponding to different current constant values. It has been shown that the MR damper model's response via the proposed approach is in good agreement with the MR damper test rig counterpart.

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Section: Introductionmentioning

confidence: 96%

Parametric Optimization of Magneto-Rheological Fluid Damper Using Particle Swarm Optimization

Azraai¹,

Priyandoko²,

Yusof³

et al. 2015

Int. J. Automot. Mech. Eng.

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“…Therefore, the Bouc–Wen model is often used to describe the piezoelectric hysteresis non-linearity. In recent years, a variety of methods (Wang et al , 2018) have been used for parameter identification of the Bouc–Wen model, such as neural network (NN) method (Wang et al , 2020), genetic algorithm (GA) (Liu and Fujii, 2015), particle swarm optimization (PSO) (Razman et al , 2014) algorithm, etc. Although the NN algorithm has good adaptability to non-linear problems, it is difficult to improve the effectiveness of the algorithm if the initial structure is not appropriate.…”

Section: Introductionmentioning

confidence: 99%

A novel identification approach of Bouc–Wen model parameter for piezoelectric hysteresis characteristic based on a modified whale optimization algorithm

Wang

Zhou

2022

COMPEL

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Purpose Due to the non-linear nature of the hysteresis behavior, the accurate identification of the parameters of the Bouc–Wen hysteresis model is still a challenging problem. The purpose of this paper is to explore the potential of a heuristic improved whale optimization algorithm (IWOA) to accurately identify the model parameters, which has never been applied to the field of piezoelectric hysteresis identification. Design/methodology/approach Based on the analysis of the Bouc–Wen model structure and WOA optimization process, an approach that can fully exploit the potential of WOA is proposed. In this work, the position updating formula is improved by introducing non-linear weights, and the convergence factor formula is modified. And thus, the iteration speed, accuracy and stability of the classical WOA can be improved. Findings The experimental results show that the model output is in good agreement with the response of the real piezoelectric platform. Compared with the standard WOA and particle swarm optimization algorithms, the search performance of the proposed IWOA is better than those two competitors in terms of convergence speed and identification accuracy. Originality/value An IWOA is proposed according to the properties of the Bouc–Wen model and piezoelectric hysteresis. It has been approved that the algorithm has a good prospect in the identification of piezoelectric hysteresis systems. Furthermore, this method is easy to implement and is a good candidate algorithm to identify Bouc–Wen model parameters.

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“…Experimental results show that the modified PI model can describe the hysteresis of PCA more precisely. The Bouc–Wen model (Li et al, 2016; Rakotondrabe, 2011; Razman et al, 2014) can describe most current hysteresis systems by selecting different model parameters with the advantages of high computational efficiency and good real-time performance. Furthermore, the inverse Bouc–Wen model which is used in the hysteresis compensate controller can also be easily obtained.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Bouc–Wen hysteresis modeling and inverse compensation for piezoelectric actuator via a genetic algorithm–based particle swarm optimization identification algorithm

Zhang

Dong

Yan

et al. 2019

Journal of Intelligent Material Systems and Structures

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The hysteresis characteristics, which commonly existed in smart materials–based actuators, play a significant role in precision control technology. In this article, a modified Bouc–Wen model which can describe the asymmetric hysteresis characteristics of piezoelectric ceramic actuators is investigated. The corresponding parameters of the modified Bouc–Wen hysteresis model are identified through a genetic algorithm–based particle swarm optimization algorithm. Compared with independent particle swarm optimization method which is easily trapped in the local extremum, the proposed genetic algorithm–based particle swarm optimization features the strong searching ability both in early global search period and the later local search period. The experimental results show that the asymmetric Bouc–Wen model identified via genetic algorithm–based particle swarm optimization algorithm are more accurate than that identified through independent particle swarm optimization or genetic algorithm approach, and the maximum displacement error and the maximum relative error between the genetic algorithm–based particle swarm optimization model and the experimental value are 0.20 µm and 14.28%, respectively, which are much smaller than that of particle swarm optimization method with 0.67 µm and 47.85% and genetic algorithm method with 0.35 µm and 25%. In order to further verify the accuracy of the identified model, the hysteresis compensation of piezoelectric ceramic actuator was realized using the feedforward controller based on the inverse Bouc–Wen model.

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Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization

Cited by 19 publications

References 17 publications

Parametric Optimization of Magneto-Rheological Fluid Damper Using Particle Swarm Optimization

Parametric Optimization of Magneto-Rheological Fluid Damper Using Particle Swarm Optimization

A novel identification approach of Bouc–Wen model parameter for piezoelectric hysteresis characteristic based on a modified whale optimization algorithm

Asymmetric Bouc–Wen hysteresis modeling and inverse compensation for piezoelectric actuator via a genetic algorithm–based particle swarm optimization identification algorithm

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