Parameter Identification for Nonlinear Dry-Friction Structure of Metallic Rubber

2011 ◽  
Vol 52-54 ◽  
pp. 494-499
Author(s):  
Yu Yan Li ◽  
Xie Qing Huang ◽  
Kai Song
Keyword(s):  
Parameter Identification ◽  
Dry Friction ◽  
Friction Angle ◽  
Element Model ◽  
Parameters Identification ◽  
Axial Stiffness ◽  
Model Parameters ◽  
Structural Element ◽  
Identification Method ◽  
Metallic Rubber

In order to reduce workload of parameter identification for nonlinear mechanical model of metallic rubber, in this paper, based on parameters identification method of static experimental curves, experiments were designed, and data were processed, further aimed at hollow cylindrical metallic rubber, nonlinear dry-friction structural element model’ parameters were identified, what’s more, friction coefficient, radial stiffness, axial stiffness, and friction angle of stainless wire under room temperature were obtained. It was proved by simulation that parameters identification method in this paper was effective and accurate. Based on this, errors of simulation were analyzed elaborately.

A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries

2021 ◽  
Vol 45 (6) ◽  
pp. 9502-9517
Author(s):  
Heng Miao ◽  
Jiajun Chen ◽  
Ling Mao ◽  
Keqing Qu ◽  
Jinbin Zhao ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Parameters Identification ◽  
Model Parameters ◽  
Identification Method

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 from Normal and Pathological Middle Ears Using a Tailored Parameter Identification Algorithm

2021 ◽  
Author(s):  
Benjamin Sackmann ◽  
Peter Eberhard ◽  
Michael Lauxmann
Keyword(s):  
Parameter Identification ◽  
Middle Ear ◽  
Measurement Data ◽  
High Sensitivity ◽  
Element Model ◽  
Great Sensitivity ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Good Representation ◽  
Adaptive Parameter

Abstract Current clinical practice is often unable to identify the causes of conductive hearing loss in the middle ear with sufficient certainty without exploratory surgery. Besides the large uncertainties due to interindividual variances, only partially understood cause-effect principles are a major reason for the hesitant use of objective methods such as wideband tympanometry in diagnosis, despite their high sensitivity to pathological changes. For a better understanding of objective metrics of the middle ear, this study presents a model that can be used to reproduce characteristic changes in metrics of the middle ear by altering local physical model parameters linked to the anatomical causes of a pathology. A finite-element model is therefore fitted with an adaptive parameter identification algorithm to results of a temporal bone study with stepwise and systematically prepared pathologies. The fitted model is able to reproduce well the measured quantities reflectance, impedance, umbo and stapes transfer function for normal ears and ears with otosclerosis, malleus fixation and disarticulation. In addition to a good representation of the characteristic influences of the pathologies in the measured quantities, a clear assignment of identified model parameters and pathologies consistent with previous studies is achieved. The identification results highlight the importance of the local stiffness and damping values in the middle ear for correct mapping of pathological characteristics, and address the challenges of limited measurement data and wide parameter ranges from literature. The great sensitivity of the model with respect to pathologies indicates a high potential for application in model-based diagnosis.

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

2009 ◽  
Vol 06 (04) ◽  
pp. 225-238 ◽  
Author(s):  
K. S. HATAMLEH ◽  
O. MA ◽  
R. PAZ
Keyword(s):  
Parameter Identification ◽  
Control Strategies ◽  
Least Square ◽  
Model Parameters ◽  
Dynamics Modeling ◽  
Recursive Least Square ◽  
Nonlinear Dynamical ◽  
Model Parameter ◽  
Identification Method ◽  
Model Parameter Identification

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs depend upon known dynamics models. The accuracy of a model depends not only on the mathematical formulae or computational algorithm of the model but also on the values of model parameters. Many model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight. A scheme of estimating an upper bound of the identification error in terms of the input data errors (or sensor errors) is also discussed.

scholarly journals Electromechanical Transient Modeling and LVRT Parameter Identification of Large Capacity Full Power Converter Wind Turbines Based on PSASP Program

2021 ◽  
Vol 2108 (1) ◽  
pp. 012002
Author(s):  
Guoqiang Lu ◽  
Xiangyu Tao ◽  
Chunmeng Chen ◽  
Jiatian Gan ◽  
Xufeng Zhao
Keyword(s):  
Parameter Identification ◽  
Wind Turbine ◽  
Low Voltage ◽  
Recovery Period ◽  
Power Converter ◽  
Control Mode ◽  
Model Parameters ◽  
Model Structure ◽  
Identification Method ◽  
Full Power

Abstract Full power converter wind turbine is the main type of wind power, so the simulation calculation needs to establish accurate model parameters. This paper analyzes the model structure of PSASP program according to its low voltage ride through control and physical characteristics, and puts forward the parameter identification method of LVRT characteristics of full power converter wind turbine, and to use the LVRT data of 5. 5MW unit for parameter identification and simulation verification. This paper proposes that the electromechanical transient simulation can ignore the part of the generator model of the full power converter wind turbine, and simulates the grid side converter with the controlled current source. The main characteristics of LVRT are determined by the control system of the converter. In order to do the parameter identification, it is necessary to calculate and analyze the control characteristics of multiple measured data. First, determine the control mode, then determine the control parameters to complete the parameter identification. In this paper, the modeling conditions and model structure of the full power converter wind turbine are confirmed. The correlation between the parameters during the LVRT fault and the parameters during the LVRT recovery period and the LVRT characteristics is analyzed. In this paper, a parameter identification method is proposed to analyze the active current and reactive current during the LVRT fault, which has strong physical significance and operability. Based on the actual LVRT characteristics of 5. 5MW wind turbine, the parameter identification and simulation are carried out to verify the correctness of the method.

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.

A dynamic model parameter identification method for quadrotors using flight data

2018 ◽  
Vol 233 (6) ◽  
pp. 1990-2002 ◽  
Author(s):  
Pin Lyu ◽  
Sheng Bao ◽  
Jizhou Lai ◽  
Shichao Liu ◽  
Zang Chen
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Unmanned Aerial Vehicle ◽  
Model Parameters ◽  
Rolling Moment ◽  
Flight Data ◽  
Model Parameter ◽  
Identification Method ◽  
Aerial Vehicle ◽  
Model Parameter Identification

The dynamic model parameter identification is important for unmanned aerial vehicle modeling and control. The unmanned aerial vehicle model parameters are usually identified through wind tunnel experiments, which are complex. In this paper, a model parameter identification method is proposed using the flight data for quadrotors. The parameters of the thrust, drag force, torque, rolling moment and pitching moment are estimated through Kalman filter. Global positioning system and inertial sensors are used as measurements. The observabilities of the model parameters and their degrees of observability are analyzed. Flight experiments are carried out to verify the proposed method. It is shown that the model parameters estimated by the proposed method have good accuracies, demonstrating the validity of the proposed method.

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