Model Parameter Identification and Simulation of Ship Power System Based on Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 482-486 ◽  
Author(s):  
Jin Hui Hu ◽  
Da Bin Hu ◽  
Jian Bo Xiao
Keyword(s):  
Mathematical Model ◽  
Power Systems ◽  
Parameter Identification ◽  
Least Squares ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Design Parameters ◽  
Model Parameters ◽  
Model Parameter ◽  
Model Parameter Identification

According to the lack of the part of the equipment design parameters of a certain type of ship power systems, the algorithm of recursive least squares for model parameter identification is studied. The mathematical model of the propulsion motor is established. The model parameters are calculated and simulated based on parameter identification method of recursive least squares. The simulation results show that a more precise mathematical model can be simple and easily obtained by using of the method.

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

scholarly journals High-Accuracy Parameter Identification Method for Equivalent-Circuit Models of Lithium-Ion Batteries Based on the Stochastic Theory Response Reconstruction

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 834
Author(s):  
Fazheng Wen ◽  
Bin Duan ◽  
Chenghui Zhang ◽  
Rui Zhu ◽  
Yunlong Shang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Equivalent Circuit ◽  
Stochastic Theory ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Battery Voltage ◽  
Model Parameter ◽  
Noise Interference ◽  
Model Parameter Identification ◽  
Response Reconstruction

The precision of battery modeling is usually determined by the identification of model parameters, which is dependent on the measured outside characteristic data of batteries. However, there is a lot of noise because of the environment noise and measurement error, leading to poor estimation accuracy of model parameters. This paper proposes a stochastic theory response reconstruction (STRR) method to reconstruct the measured battery voltage data, which can eliminate the noise interference and ensure high-precision model parameter identification. The relationship between the battery voltage and current is established based on the the second-order equivalent circuit model (ECM) by the convolution theorem, and the impulse function is calculated by the correlation function between the measured voltage and current. Then, the battery voltage is reconstructed and used to identify model parameters with the recursive least squares (RLS) algorithm. All data for model parameter identification is produced through the pseudo random binarysequence (PRBS) excitation signal. Finally, the Urban Dynamometer Driving Schedule (UDDS) and Federal Urban Driving Schedule (FUDS) tests are conducted to validate the performance of the proposed method. Experimental results show that when compared with the traditional solution using low-pass filter, the proposed method can eliminate the noise interference more effectively and has higher identification accuracy.

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 The identification of a device based on a Peltier element by the least squares method

2020 ◽  
pp. 17-27
Author(s):  
Vladimir Grinkevich ◽  
Keyword(s):  
Mathematical Model ◽  
Least Squares ◽  
Least Squares Method ◽  
Control Object ◽  
Object Identification ◽  
Model Parameters ◽  
Peltier Element ◽  
Transport Delay ◽  
Non Linear ◽  
The Mathematical Model

The evaluation of the mathematical model parameters of a non-linear object with a transport delay is considered in this paper. A temperature controlled stage based on a Peltier element is an identification object in the paper. Several input signal implementations are applied to the input of the identification object. The least squares method is applied for the calculation of the non-linear differential equitation parameters which describe the identification object. The least squares method is used due to its simplicity and the possibility of identification non-linear objects. The parameters values obtained in the process of identification are provided. The plots of temperature changes in the temperature control system with a controller designed based on the mathematical model of the control object obtained as a result of identification are shown. It is found that the mathematical model obtained in the process of identification may be applied to design controllers for non-linear systems, in particular for a temperature stage based on a Peltier element, and for self-tuning controllers. However, the least square method proposed in the paper cannot estimate the transport delay time. Therefore it is required to evaluate the time delay by temperature transient processes. Dynamic object identification is applied when it is required to obtain a mathematical model structure and evaluate the parameters by an input and output control object signal. Also, identification is applied for auto tuning of controllers. A mathematical model of a control object is required to design the controller which is used to provide the required accuracy and stability of control systems. Peltier elements are applied to design low-power and small- size temperature stage . Hot benches based on a Peltier element can provide the desired temperature above and below ambient temperature.

MEMS Gyro Random Drift Model Parameter Identification Based on Two-Stage Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 1044-1047 ◽  
Author(s):  
Zhao Hua Liu ◽  
Jia Bin Chen ◽  
Yu Liang Mao ◽  
Chun Lei Song
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Average ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Random Drift ◽  
Two Stage ◽  
Autoregressive Moving Average Model ◽  
Drift Model ◽  
Recursive Least Squares Method

Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

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 Model parameter identification from measurement data for dynamic torque calibration – Measurement results and validation

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (3) ◽  
pp. 55 ◽  
Author(s):  
Leonard Klaus
Keyword(s):  
Parameter Identification ◽  
Transfer Functions ◽  
Dynamic Properties ◽  
Measurement Data ◽  
Model Parameters ◽  
Dynamic Calibration ◽  
Measuring Device ◽  
Model Parameter ◽  
Calibration Measurement ◽  
Model Parameter Identification

<p><span lang="EN-US">The dynamic calibration of torque transducers requires the </span><span lang="EN-GB">modelling</span><span lang="EN-US"> of the measuring device and of the transducer under test. The transducer's dynamic properties are described by means of model parameters, which are going to be identified from measurement data. To be able to do so, two transfer functions are calculated. In this paper, the transfer functions and the procedure for the model parameter identification are presented. Results of a parameter identification of a torque transducer are also given, and the validity of the identified parameters is </span><span lang="EN-GB">analysed</span><span lang="EN-US"> by comparing the results with independent measurements. The successful parameter identification is a prerequisite for a model-based dynamic calibration of torque transducers.</span></p>

Study on the Giant Magnetostrictive Materials with Actuator Displacement Model Parameter Identification

2013 ◽  
Vol 830 ◽  
pp. 37-40
Author(s):  
Jia Deng
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parameter Identification ◽  
Nonlinear Model ◽  
Simulated Annealing Algorithm ◽  
Model Parameters ◽  
Accurate Identification ◽  
Model Parameter ◽  
Annealing Algorithm ◽  
Model Parameter Identification

Accurate identification of model parameters is to improve the giant magnetostrictive precision displacement control key, For single algorithm is difficult to achieve for giant magnetostrictive hysteresis nonlinear model parameters accurately identify problems, in this paper, the genetic algorithm and simulated annealing algorithm fusion, First, quick search ability of genetic algorithm are used to get a better community, recycle kick ability of simulated annealing algorithm to to adjust and optimize the whole group, Presented an improved genetic simulated annealing algorithm, And its application to the giant magnetostrictive actuator displacement hysteresis nonlinear model parameter identification. The algorithm combines the advantages of genetic algorithm and simulated annealing algorithm, both the faster convergence speed, and improves the precision and quality of the optimal solution.

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