Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

Wang, Geng; Ke-xin, Zhou; Zhang, Yeming

doi:10.1142/s0217984918501312

Cited by 12 publications

(6 citation statements)

References 23 publications

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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. GA has strong ability in local space search, but it has some defects such as prematurity and poor stability.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…When the food source gets a larger fitness value, the right solution will be selected by more chance. To uncover novel food source, the following rules must be obeyed when the used bees become scout bees (Wang et al, 2018;Xiang and An, 2013):…”

Section: Classical Bee Colony Algorithmmentioning

confidence: 99%

“…The well-performed bee colony algorithm seems to be a good candidate in parameter identification of nonlinear systems. In particular, the ability of bee colony algorithm to identify piezoelectric nonlinear system has also been verified (Wang et al, 2018), but there are still very few similar literatures. This may result from the insufficiency of the population search equations, which limits the application of bee colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

Identification of Duhem-type piezoelectric hysteresis using a modified bee colony algorithm

Chen

Yang

2019

COMPEL

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Purpose The determination of parameters of Duhem model that can describe piezoelectric hysteresis is usually a great challenge. The purpose of this paper is to find a way to identify the parameters of Duhem model by using a modified bee colony algorithm. Design/methodology/approach The promising bee colony algorithm has great potential to identify hysteresis nonlinearity, but has not yet been used to identify the Duhem-type hysteresis in the literatures. To explore this possibility, the classical bee colony algorithm is modified to enhance its performance regarding both searching capability and convergence speed. In the modification, the current optimal solution is used to guide the search direction, which can balance the local and global searching ability. Moreover, a new searching formula for scout bees is proposed to enhance the convergence ability of the algorithm. Findings Through a series of experiments, the modified algorithm can attain the optimal parameters with a 0.61 µm peak valley error and a 0.12 µm root-mean-square error. Compared to the particle swarm optimization and classical bee colony algorithms, the modified bee colony algorithm can reach higher parameter identification accuracy. Based on 50 trials, the robustness of the posed algorithm was also proved. Originality/value The well-performed modified bee colony algorithm is a good candidate in parameter identification of Duhem-type hysteresis nonlinear systems. As there is no work studying the parameter identification of Duhem model using a bee colony algorithm in the literatures, this work closed this gap and explored the ability of bee colony algorithm to identify piezoelectric hysteresis with superb accuracy and robustness.

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“…In essence, model parameter identification is an optimization process, where the optimization target is to minimize the errors between real outputs of PZT actuators and model predictions. Swarm evolutionary algorithms have been employed for model parameter identification of PZT actuators, such as PSO [32], genetic algorithm (GA) [33], artificial bee colony (ABC) algorithm [34,35], etc. In [25], hybrid GA and PSO methods were employed to identify the parameters of BW model for hysteresis response characterization of PZT actuators.…”

Section: Introductionmentioning

confidence: 99%

Modeling and identification of nonlinear hysteresis behavior of piezoelectric actuators using a computationally efficient phenomenological model and modified cuckoo search algorithm

Xie¹,

Cui²,

Yu³

et al. 2022

Smart Mater. Struct.

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Hysteresis, an intrinsic characteristic of PZT actuators, has been demonstrated to dramatically reduce the capability and stability of the system. This paper proposes a novel computationally efficient model to describe nonlinear and hysteresis behaviors of PZT actuators. First of all, the model parameters are analyzed to investigate their effects on the output response. Then, a modified cuckoo search (CS) algorithm is used to identify the model parameters, without falling into the local optimum problems through introducing adaptive egg discovery probability and step length control factor. Further, the performance of the proposed model is validated using experimental data, via the comparison with classical Bouc-Wen and Prandtl-Ishlinskii hysteresis models. Finally, the rate-dependence of the parameters of proposed model is analyzed, which contributes to a generalized hysteresis model for the compensation control application of PZT actuators.

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Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

Cited by 12 publications

References 23 publications

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

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

Identification of Duhem-type piezoelectric hysteresis using a modified bee colony algorithm

Modeling and identification of nonlinear hysteresis behavior of piezoelectric actuators using a computationally efficient phenomenological model and modified cuckoo search algorithm

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