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 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.

A new identification method of the Stribeck friction model based on limit cycles

2014 ◽  
Vol 228 (15) ◽  
pp. 2678-2683 ◽  
Author(s):  
LL Liu ◽  
ZY Wu
Keyword(s):  
Parameter Identification ◽  
Limit Cycle ◽  
Limit Cycles ◽  
Friction Model ◽  
Limit Cycle Oscillation ◽  
Characteristic Parameters ◽  
Chain Code ◽  
Shape Characteristic ◽  
Identification Method ◽  
Model Based

This paper presents a new parameter identification method of the Stribeck friction model based on limit cycles. A single degree of freedom mass spring system driven by a belt is studied, and the Stribeck friction model is established between the mass and belt. Limit cycle oscillation will occur when the system is unstable. The limit cycle curve is described by some main shape characteristic parameters using the modified Freeman chain code method. Thus, the Stribeck friction parameters can be identified by using the ergodic search method to minimize the Euclidean distance of the theoretical and identified limit cycle shape characteristic parameters. The parameter identification method based on limit cycles is different from the traditional identification methods. It only needs the displacement and velocity responses of the system instead of the measurement of the friction force or motor voltage/current. All of these works can provide the reference for the research work of the friction parameter identification.

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.

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.

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.

Load Dependent Hysteresis Model for GMM and its Parameter Identification

2012 ◽  
Vol 226-228 ◽  
pp. 2385-2389 ◽  
Author(s):  
Guang Hui Chang ◽  
Shi Jian Zhu ◽  
Jing Jun Lou
Keyword(s):  
Vibration Control ◽  
Smart Materials ◽  
Active Vibration Control ◽  
Least Square Method ◽  
Least Square ◽  
Hysteresis Curve ◽  
Model Parameters ◽  
Hysteresis Model ◽  
Magnetostrictive Actuators ◽  
Active Vibration

This paper focuses on the development of load-dependent hysteresis model for Giant magnetostrictive materials (GMM). GMM are a class of smart materials and which are used mostly as actuators for active vibration control. Magnetostrictive actuators can deliver high ouput forces and relatively high displacements. Here, Terfenol-D, a magnetostrictive material is studied. Unlike the hysteresis seen in magnetic materials, The shape of Terfenol-D hysteresis curve changes significantly if the load is changed. To meet performance requirements for active vibration control, an accurate hysteresis model is needed. By modeling the Gibbs energy for each dipole and the equilibrium states, load-dependent hysteresis of GMM is modeled. Then a new PSO-LSM algorithm is brought forward by combing the Particle Swarm Optimization (PSO) with the least square method (LSM).Throughout this algorithm the model parameters were identified. The model results and experimental data were compared at different loads. The simulation results show that the load-dependent hysteresis model optimized by PSO-LSM yields outstanding performance and perfect accuracy.

Parameter Identification of a Stress Relaxation Model Based on Differential Evolution Algorithm

2013 ◽  
Vol 842 ◽  
pp. 482-485
Author(s):  
Wei Wei Zhang ◽  
Hong Xu
Keyword(s):  
Stress Relaxation ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Differential Evolution Algorithm ◽  
Parameter Determination ◽  
Model Parameters ◽  
Relaxation Model ◽  
Model Based ◽  
De Algorithm ◽  
Creep And Stress Relaxation

In order to determine the parameters of a stress relaxation model based on Altenbach-Gorash-Naumenko creep equations, an efficient parameter identification scheme is discussed. The differential evolution (DE) algorithm is used in the identification procedure with a modified forward-Euler scheme. The model parameters of 1Cr-0.5Mo-0.25V stainless steel bolting material at 500°C have been determined, and the creep and stress relaxation behaviors have been calculated. Comparing with a step-by-step model parameter determination technology and the genetic algorithm (GA), it shows that the DE algorithm has better convergence property and suitability for parallelization, and no need of initial guesses close to the solution. Results indicate that the optimum solutions can be obtained more easily by DE algorithm than others.

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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