Bouc-Wen Hysteresis Model Parameter Identification by Means of Hybrid Intelligent Technique

2010 ◽  
Vol 108-111 ◽  
pp. 1397-1402 ◽  
Author(s):  
Mei Ying Ye ◽  
Hui Jiang ◽  
You Sheng Xu ◽  
Xiao Dong Wang
Keyword(s):  
Parameter Identification ◽  
Optimization Method ◽  
Model Parameters ◽  
Hysteresis Model ◽  
Problem Space ◽  
High Quality ◽  
Simulation Experiments ◽  
Local Optima ◽  
Intelligent Technique ◽  
Marquardt Algorithm

A hybrid intelligent technique is proposed to identify Bouc-Wen hysteresis model parameters. This intelligent technique is based on a hybrid of genetic algorithm (GA) and Levenberg–Marquardt algorithm (LMA). In the hybrid intelligent technique, the GA, a popular evolutionary optimization method, firstly searches the entire problem space to get a set of roughly estimated solutions. The LMA, a well-known numerical method, then performs a local optima search in order to carry out further optimizations. The performance of the hybrid intelligent technique is compared with GA method in terms of parameter accuracy. The simulation experiments of Bouc-Wen hysteresis model with known parameters are illustrated to show that a high quality solution can be achieved by means of the hybrid intelligent technique. The concept of hybrid intelligent technique may benefit the parameter identification in diverse hysteresis model problems.

Hybrid Optimization Approach for Evaluating Photovoltaic Cell Parameters

2013 ◽  
Vol 373-375 ◽  
pp. 1261-1264
Author(s):  
Mei Ying Ye
Keyword(s):  
Cell Model ◽  
Computation Time ◽  
Photovoltaic Cell ◽  
Evaluation Procedure ◽  
Model Parameters ◽  
Optimization Approach ◽  
Problem Space ◽  
Local Optima ◽  
Cell Parameters ◽  
Intelligent Technique

A new hybrid intelligent technique is proposed to evaluate photovoltaic cell model parameters in this paper. The intelligent technique is based on a hybrid of genetic algorithm (GA) and LevenbergMarquardt algorithm (LMA). In the proposed hybrid intelligent technique, the GA firstly searches the entire problem space to get a set of roughly estimated solutions, i.e. near-optimal solutions. Then the LMA performs a local optima search in order to carry out further optimizations. An example has been used to demonstrate the evaluation procedure in order to test the performance of the proposed approach. The results show that the proposed technique has better performance than the GA approach in terms of the objective function value, the computation time and the reconstructedI-Vcurve shape.

Material Parameter Identification of Cazacu’s Model for Ti6Al4V Using the Simulated Annealing Algorithm

2010 ◽  
Vol 636-637 ◽  
pp. 1125-1130 ◽  
Author(s):  
Gaëtan Gilles ◽  
Anne Marie Habraken ◽  
Laurent Duchêne
Keyword(s):  
Simulated Annealing ◽  
Parameter Identification ◽  
Simulated Annealing Algorithm ◽  
Optimization Method ◽  
Mechanical Tests ◽  
Global Optimum ◽  
Material Parameter Identification ◽  
Local Optima ◽  
Material Parameters ◽  
The One

Phenomenological yield criteria are generally described by many material parameters. A technique to identify these parameters is required to find the best fit to the results of the mechanical tests. The parameter identification by the classical simulated annealing technique is presented in this paper. This algorithm, based on works by Metropolis et al, is a global optimization method that distinguishes between different local optima to reach the global optimum. The anisotropic model used in this study is the one proposed by Cazacu et al. To prove the efficiency of the proposed algorithm, the material parameters of Ti6Al4V titanium alloy are identified and compared with those obtained using different identification procedures and the same experimental data.

scholarly journals An Asymmetric Hysteresis Model and Parameter Identification Method for Piezoelectric Actuator

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Haichen Qin ◽  
Ningbin Bu ◽  
Wei Chen ◽  
Zhouping Yin
Keyword(s):  
Parameter Identification ◽  
Smart Materials ◽  
Model Parameters ◽  
Hysteresis Model ◽  
Pzt Actuator ◽  
Modified Particle Swarm Optimization ◽  
Identification Method ◽  
Model Based ◽  
Asymmetric Factor ◽  
Hysteresis Behaviour

Hysteresis behaviour degrades the positioning accuracy of PZT actuator for ultrahigh-precision positioning applications. In this paper, a corrected hysteresis model based on Bouc-Wen model for modelling the asymmetric hysteresis behaviour of PZT actuator is established by introducing an input biasφand an asymmetric factorΔΦinto the standard Bouc-Wen hysteresis model. A modified particle swarm optimization (MPSO) algorithm is established and realized to identify and optimize the model parameters. Feasibility and effectiveness of MPSO are proved by experiment and numerical simulation. The research results show that the corrected hysteresis model can represent the asymmetric hysteresis behaviour of the PZT actuator more accurately than the noncorrected hysteresis model based on the Bouc-Wen model. The MPSO parameter identification method can effectively identify the parameters of the corrected and noncorrected hysteresis models. Some cases demonstrate the corrected hysteresis model and the MPSO parameter identification method can be used to model smart materials and structure systems with the asymmetric hysteresis behaviour.

scholarly journals Determination of a Hysteresis Model Parameters with the Use of Different Evolutionary Methods for an Innovative Hysteresis Model

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 201 ◽  
Author(s):  
Marko Jesenik ◽  
Marjan Mernik ◽  
Mladen Trlep
Keyword(s):  
Mathematical Expression ◽  
Optimization Method ◽  
Precise Determination ◽  
Model Material ◽  
Model Parameters ◽  
Magnetization Process ◽  
Hysteresis Model ◽  
Bee Colony ◽  
Two Parameters

For precise modeling of electromagnetic devices, we have to model material hysteresis. A Genetic Algorithm, Differential Evolution with three different strategies, teaching–learning-based optimization and Artificial Bee Colony, were used for testing seven different modified mathematical expressions, and the best combination of mathematical expression and solving method was used for hysteresis modeling. The parameters of the hysteresis model were determined based on the measured major hysteresis loop and first-order reversal curves. The model offers a simple determination of the magnetization procedure in the areas between measured curves, with the only correction of two parameters based on only two known points in the magnetization process. It was tested on two very different magnetic materials, and results show good agreement between the measured and calculated curves. The calculated curves between the measured curves have correct shapes. The main difference between our model and other models is that, in our model, each measured curve, major and reversal, is described with different parameters. The magnetization process between measured curves is described according to the nearest measured curve, and this ensures the best fit for each measured curve. In other models, there is mainly only one curve, a major hysteresis or magnetization curve, used for the determination of the parameters, and all other curves are then dependent on this curve. Results confirm that the evolutionary optimization method offers a reliable procedure for precise determination of the parameters.

scholarly journals Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage using Modified Particle Swarm Optimization

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 315
Author(s):  
Khubab Ahmed ◽  
Peng Yan ◽  
Su Li
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Model Identification ◽  
Particle Swarm ◽  
Identification Problem ◽  
Search Space ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Local Optima ◽  
Modified Particle Swarm Optimization

This paper presents modeling and parameter identification of the Duhem model to describe the hysteresis in the Piezoelectric actuated nano-stage. First, the parameter identification problem of the Duhem model is modeled into an optimization problem. A modified particle swarm optimization (MPSO) technique, which escapes the problem of local optima in a traditional PSO algorithm, is proposed to identify the parameters of the Duhem model. In particular, a randomness operator is introduced in the optimization process which acts separately on each dimension of the search space, thus improving convergence and model identification properties of PSO. The effectiveness of the proposed MPSO method was demonstrated using different benchmark functions. The proposed MPSO-based identification scheme was used to identify the Duhem model parameters; then, the results were validated using experimental data. The results show that the proposed MPSO method is more effective in optimizing the complex benchmark functions as well as the real-world model identification problems compared to conventional PSO and genetic algorithm (GA).

scholarly journals ANALYSIS OF MODERN TECHNIQUES FOR SOFTWARE OPTIMIZATION

2021 ◽  
Vol 10 (7) ◽  
pp. 46-55
Author(s):  
Okardi, Biobele Ojekudo ◽  
Nathaniel Akofure
Keyword(s):  
Genetic Algorithm ◽  
Traditional Method ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
High Quality ◽  
Artificial Intelligent ◽  
Intelligent Technique ◽  
Artificial Intelligent Technique ◽  
Modern Optimization

Traditional Methods of optimization have failed to meet up the rapid changing world in the demand of high quality and accuracy in solution delivery. Optimization literally means looking for the best possible or most desired solution to a problem. Optimization techniques are basically classified into three groups, namely; the Traditional Method, Artificial Intelligent Method, and Hybrid Artificial Intelligent technique. In this paper, an attempt is made to review literatures on different modern optimization techniques for application in various disciplines. A general review was made on some of the modern optimization methods such as Genetic Algorithm, Ant colony method, Honey Bee optimization method, and Simulated Annealing optimization.

Parameter identification of Jiles–Atherton model using the chaotic optimization method

2018 ◽  
Vol 37 (6) ◽  
pp. 2067-2080 ◽  
Author(s):  
Vesna Rubežić ◽  
Luka Lazović ◽  
Ana Jovanović
Keyword(s):  
Parameter Identification ◽  
Hysteresis Loop ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Optimization Method ◽  
Pattern Search ◽  
Simultaneous Optimization ◽  
Hysteresis Model ◽  
Suitable Alternative ◽  
Content Type

Purpose The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model. Design/methodology/approach The J-A model has five parameters which are assigned with physical meaning and whose determination is demanding. To determine these parameters, the fitness function, which represents the difference between the measured and the modeled hysteresis loop, is formed. Optimal parameter values are the values that minimize the fitness function. Findings The parameters of J-A model for three magnetic materials are determined. The model with the optimal parameters is validated using measured data and comparison with particle swarm optimization algorithm, genetic algorithm, pattern search and simulated annealing algorithm. The results show that the proposed method provides better agreement between measured and modeled hysteresis loop than other methods used for comparison. The proposed method is also suitable for simultaneous optimization of multiple hysteresis loops. Originality/value Chaotic optimization method is implemented for the first time for J-A model parameter identification. Numerical comparisons with results obtained with other optimization algorithms demonstrate that this method is a suitable alternative in parameters identification of J-A hysteresis model. Furthermore, this method is easy to implement and set up.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

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