Parameter Identification of Inelastic Structures

1999 ◽  
Author(s):  
H. Zhang ◽  
Y. Yang ◽  
G. C. Foliente ◽  
F. Ma
Keyword(s):  
Parameter Identification ◽  
Gradient Methods ◽  
Model Parameters ◽  
Inelastic Behavior ◽  
Hysteretic Model ◽  
Differential Model ◽  
Inelastic Structures ◽  
Curve Fit ◽  
Successful Design ◽  
Strong Winds

Abstract Structures often exhibit nonlinear and inelastic behavior in the form of hysteresis loop under severe loads associated with earthquake, austere winds and waves. Hysteresis is particularly important in depicting the nonlinear response of wood buildings, braced steel frames, reinforced concrete, and structures with a high proportion of composite materials. A practical model of hysteresis that would match experimental observations on real structures is needed for the successful design of structures against earthquakes and strong winds. Two different time-domain system identification algorithms will be presented in this report to estimate the parameters of an extended Bouc-Wen hysteretic model. This version of the differential model of hysteresis can curve-fit practically any hysteresis trace with a suitable choice of the model parameters. Thirteen control parameters are included in the model. The parameter identification algorithms presented in this report include the constrained simplex and generalized reduced gradient methods. Noise filtering techniques and constraints will also be used in this study to assist in parameter identification. The effectiveness of the proposed algorithms will be demonstrated through simulations of nonlinear systems with pinching and degradation characteristics. Due to very modest computing requirements, the proposed identification algorithms can be acceptable as a basic tool for estimating hysteretic parameters in engineering design.

On the Identification of Hysteresis in Inelastic Systems

2001 ◽  
Author(s):  
Fai Ma
Keyword(s):  
Kalman Filter ◽  
Gradient Methods ◽  
Design Tool ◽  
Control Parameters ◽  
Internal Constraints ◽  
Inelastic Structures ◽  
Curve Fit ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient ◽  
Reduced Gradient Methods

Abstract The generalized model of differential hysteresis contains thirteen control parameters with which it can curve-fit practically any hysteretic trace. Three identification algorithms are developed to estimate the control parameters of hysteresis for different classes of inelastic structures. These algorithms are based upon the simplex, extended Kalman filter, and generalized reduced gradient methods. Novel techniques such as global search and internal constraints are incorporated to facilitate convergence and stability. Effectiveness of the devised algorithms is demonstrated through simulations of two inelastic systems with both pinching and degradation characteristics in their hysteretic traces. Due to very modest computing requirements, these identification algorithms may become acceptable as a design tool for mapping the hysteretic traces of inelastic structures.

Parameter Identification for HAPC System in Hot Tandem Mill

2010 ◽  
Vol 154-155 ◽  
pp. 781-786
Author(s):  
Xu Li ◽  
Wen Xue Zhang ◽  
Dian Hua Zhang ◽  
Dan Yan
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Parameter Identification ◽  
Actual Data ◽  
Automation System ◽  
Model Parameters ◽  
Simulation Data ◽  
Matlab Software ◽  
Identification Method ◽  
Curve Fit

Under condition that exact values of model parameters can not be calculated accurately in hot tandem mill system and change with the time passing, model parameters are identified by adopting identification method based on the parameter model and sampling the datum on site; Basic automation system is used for the sampling of actual data, MATLAB software is adopted for curve fit. By comparing the experimental data and simulation data, the consequence of simulation testifies the accuracy of identified mathematical model.

Parameter identification of a differentiable Bouc-Wen model using constrained extended Kalman filter

2020 ◽  
pp. 147592172092943
Author(s):  
Dan Li ◽  
Yang Wang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Structural Safety ◽  
Model Parameters ◽  
Suitable Model ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
System Equations ◽  
Parameter Constraints

Hysteresis is of critical importance to structural safety under severe dynamic loading conditions. One of the widely used hysteretic models for civil structures is the Bouc-Wen model, the effectiveness of which depends on suitable model parameters. The locally non-differentiable governing equation of the conventional Bouc-Wen model poses difficulty on existing identification algorithms, especially the extended Kalman filter, which relies on linearized system equations to propagate state estimates and covariance. In addition, the standard extended Kalman filter usually does not incorporate parameter constraints, and therefore may result in unreasonable estimates. In this article, a modified and differentiable Bouc-Wen model, together with a constrained extended Kalman filter (CEKF), is proposed to identify the hysteretic model parameters in a reliable way. The partial derivatives of the differentiable Bouc-Wen model with respect to hysteretic parameters can be easily calculated for implementing the identification algorithm. Constrained extended Kalman filter restricts the Kalman gain to ensure that the estimates of parameters satisfy constraints from physical laws. Parameter identification using simulated and experimental data collected from a four-story structure demonstrates that constrained extended Kalman filter can achieve more reliable identification results than the standard extended Kalman filter.

Parameter Sensitivity Analysis of the Extended Bouc-Wen Model of Hysteresis

2002 ◽  
Author(s):  
Haochuan Zhang ◽  
Fai Ma
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Simplified Model ◽  
Model Parameters ◽  
Hysteretic Model ◽  
Structural Damping ◽  
Parameter Sensitivity Analysis ◽  
Differential Model ◽  
Global Sensitivity

The extended Bouc-Wen differential model is one of the most widely accepted phenomenological models of hysteresis in computational mechanics. It is routinely used in the characterization of structural damping and in system identification. In this paper, the differential model of hysteresis is carefully re-examined and two significant issues are uncovered. First, it is found that the unspecified parameters of the model are not independent. One of the model parameters can be eliminated through suitable transformations in the parameter space. Second, through local and global sensitivity analysis, it is found that some parameters of the hysteretic model are rather insensitive. If these insensitive parameters are set to constant values, a greatly simplified model is obtained.

System Identification and Response Prediction of Deteriorating Structures

2006 ◽  
Author(s):  
C. H. Ng ◽  
N. Ajavakom ◽  
F. Ma
Keyword(s):  
System Identification ◽  
Real Life ◽  
Response Prediction ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
Sea Waves ◽  
Major Barrier ◽  
Differential Model ◽  
Successful Design ◽  
Load Displacement

The lack of a fundamental theory of hysteresis is a major barrier to successful design of structures against deterioration associated with earthquakes, high winds, and sea waves. Development of a practical model of degrading structures that would match experimental observations is an important task. This paper has a two-fold objective. First, a superior system identification algorithm is devised to estimate the unspecified parameters in a differential model of hysteresis from experimental load-displacement traces. This algorithm is based upon the latest theory of genetic evolution and it will be streamlined through global sensitivity analysis. Second, the utility of identification of hysteresis is demonstrated through response prediction. Suppose a hysteretic model is generated with a given load-displacement trace. It will be shown experimentally that the model will predict the response of the same system driven by other cyclic loads. The requirements for precise prediction will be addressed. Through identification of hysteresis, it becomes possible to assess, for the first time in analysis, the performance of a real-life structure that has previously been damaged. In the open literature, there is not any other method that can perform the same task.

Identification and Performance Prediction of Nonlinear Degrading Structures

2007 ◽  
Author(s):  
Nopdanai Ajavakom ◽  
Ching H. Ng ◽  
Fai Ma
Keyword(s):  
Real Life ◽  
Response Prediction ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
Sea Waves ◽  
Major Barrier ◽  
Differential Model ◽  
Successful Design ◽  
Load Displacement ◽  
And Performance

The lack of a fundamental theory of hysteresis is a major barrier to successful design of structures against deterioration associated with earthquakes, high winds, and sea waves. Development of a practical model of degrading structures that would match experimental observations is an important task. This paper has a two-fold objective. First, a superior system identification algorithm is devised to estimate the unspecified parameters in a differential model of hysteresis from experimental load-displacement traces. This algorithm is based upon the latest theory of genetic evolution and it will be streamlined through global sensitivity analysis. Second, the utility of identification of hysteresis is demonstrated through nonlinear response prediction, which is important in structural design. Suppose a hysteretic model is generated with a given load-displacement trace. It will be shown experimentally that the model will predict the response of the same system driven by other cyclic loads. The requirements for accurate prediction will be addressed. Through identification of hysteresis, it becomes possible to assess the performance of a real-life structure that has previously been damaged. In the open literature, there is not any other method that can perform the same task.

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.

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

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