Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

Friction Model ◽

Carbon Surface ◽

Model Parameters ◽

Model Parameter ◽

Brake Pressure ◽

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.830.37 ◽

2013 ◽

Vol 830 ◽

pp. 37-40

Author(s):

Jia Deng

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Nonlinear Model ◽

Simulated Annealing Algorithm ◽

Model Parameters ◽

Accurate Identification ◽

Model Parameter ◽

Annealing Algorithm ◽

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.

Recent Advances in Parallel Virtual Machine and Message Passing Interface - Lecture Notes in Computer Science ◽

A Method for Model Parameter Identification Using Parallel Genetic Algorithms

10.1007/3-540-48158-3_36 ◽

1999 ◽

pp. 291-298 ◽

Cited By ~ 2

Author(s):

J. I. Hidalgo ◽

M. Prieto ◽

J. Lanchares ◽

F. Tirado ◽

B. de Andrés ◽

...

Keyword(s):

Genetic Algorithms ◽

Model Parameter ◽

Parallel Genetic Algorithms ◽

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.482 ◽

2012 ◽

Vol 220-223 ◽

pp. 482-486 ◽

Cited By ~ 1

Author(s):

Jin Hui Hu ◽

Da Bin Hu ◽

Jian Bo Xiao

Keyword(s):

Mathematical Model ◽

Power Systems ◽

Least Squares ◽

Least Squares Method ◽

Recursive Least Squares ◽

Design Parameters ◽

Model Parameters ◽

Model Parameter ◽

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.

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

International Journal of Information Acquisition ◽

10.1142/s0219878909001977 ◽

2009 ◽

Vol 06 (04) ◽

pp. 225-238 ◽

Cited By ~ 6

Author(s):

K. S. HATAMLEH ◽

O. MA ◽

R. PAZ

Keyword(s):

Control Strategies ◽

Least Square ◽

Model Parameters ◽

Dynamics Modeling ◽

Recursive Least Square ◽

Nonlinear Dynamical ◽

Model Parameter ◽

Identification Method ◽

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.

Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control ◽

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

10.1115/dscc2013-3776 ◽

2013 ◽

Cited By ~ 1

Author(s):

Yiran Hu ◽

Yue-Yun Wang

Keyword(s):

Least Squares ◽

State Estimation ◽

Real Time ◽

Recursive Least Squares ◽

Model Parameters ◽

Identification Algorithm ◽

Model Parameter ◽

Battery Model ◽

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.

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

ACTA IMEKO ◽

10.21014/acta_imeko.v5i3.318 ◽

2016 ◽

Vol 5 (3) ◽

pp. 55 ◽

Cited By ~ 2

Author(s):

Leonard Klaus

Keyword(s):

Transfer Functions ◽

Dynamic Properties ◽

Measurement Data ◽

Model Parameters ◽

Dynamic Calibration ◽

Measuring Device ◽

Model Parameter ◽

Calibration Measurement ◽

The dynamic calibration of torque transducers requires the modelling 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 analysed by comparing the results with independent measurements. The successful parameter identification is a prerequisite for a model-based dynamic calibration of torque transducers.

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

A dynamic model parameter identification method for quadrotors using flight data

10.1177/0954410018767754 ◽

2018 ◽

Vol 233 (6) ◽

pp. 1990-2002 ◽

Cited By ~ 3

Author(s):

Pin Lyu ◽

Sheng Bao ◽

Jizhou Lai ◽

Shichao Liu ◽

Zang Chen

Keyword(s):

Dynamic Model ◽

Unmanned Aerial Vehicle ◽

Model Parameters ◽

Rolling Moment ◽

Flight Data ◽

Model Parameter ◽

Identification Method ◽

Aerial Vehicle ◽

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.

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

Electronics ◽

10.3390/electronics8080834 ◽

2019 ◽

Vol 8 (8) ◽

pp. 834

Author(s):

Fazheng Wen ◽

Bin Duan ◽

Chenghui Zhang ◽

Rui Zhu ◽

Yunlong Shang ◽

...

Keyword(s):

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.

Condition Monitoring and Assessment of Power Transformers Using Computational Intelligence - Power Systems ◽

Thermal Model Parameter Identification and Verification Using Genetic Algorithm

10.1007/978-0-85729-052-6_5 ◽

2011 ◽

pp. 73-94

Author(s):

W. H. Tang ◽

Q. H. Wu

Keyword(s):

Genetic Algorithm ◽

Thermal Model ◽

Model Parameter ◽

Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing ◽

Active Excitation for Battery Model Parameter Identification

10.1115/dscc2014-6059 ◽

2014 ◽

Author(s):

Yonghua Li ◽

Hai Yu

Keyword(s):

Real World ◽

Input Signal ◽

Identification Accuracy ◽

Model Parameters ◽

Model Parameter ◽

Battery Model ◽

Battery Power ◽

Reference Parameter ◽

In this paper an active excitation approach to battery model parameter identification is discussed. Based on begin-of-life battery model, it is possible to establish a reference parameter table (either fixed, or adaptively learned), and based on such reference parameter table, as well as by analysing battery input signal, active excitation request may be generated. Active excitation is achieved based on maintaining overall torque level with regard to drive input, while adjusting both engine and battery power output (and input). Both conditions for active excitation request, as well as active excitation generation approaches, are presented in detail. Simulation examples using production electrified vehicle battery model parameters and real world drive cycles demonstrate that the proposed approach indeed improves battery model parameter identification accuracy.