Analysis of a Platform for Measuring Moments and Products of Inertia of Large Vehicles

1976 ◽  
Vol 98 (2) ◽  
pp. 186-195
Author(s):  
D. Orne ◽  
T. Schmitz
Keyword(s):  
System Identification ◽  
Test Data ◽  
Normal Modes ◽  
Free Vibration Analysis ◽  
Identification Algorithm ◽  
Test Configuration ◽  
Truncation Errors ◽  
Sample Test ◽  
Instantaneous Center ◽  
Cable Lines

A rigid platform symmetrically supported by four sloping cables is proposed for measuring the center-of-gravity coordinates and the moments and products of inertia of large vehicles such as buses, trucks, and trailers. In addition to a torsional degree-of-freedom, the system undergoes pitch and roll motions about axes through the system instantaneous center which lies directly below the center of the platform at the intersection of the cable lines-of-action under quiescent conditions. The natural frequencies and normal modes of the freely vibrating loaded platform are used as inputs to a linearized System Identification Algorithm for computing the inertia properties of the test vehicle. Hypothetical test data generated from the Free Vibration Analysis of a sample test configuration are used to evaluate the sensitivity of the System Identification Algorithm to inaccuracies in test data or to truncation errors in computation.

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.

scholarly journals System Identification of Alfred Zampa Memorial Bridge Using Dynamic Field Test Data

2009 ◽  
Vol 135 (1) ◽  
pp. 54-66 ◽  
Author(s):  
Xianfei He ◽  
Babak Moaveni ◽  
Joel P. Conte ◽  
Ahmed Elgamal ◽  
Sami F. Masri
Keyword(s):  
System Identification ◽  
Field Test ◽  
Test Data ◽  
Dynamic Field

scholarly journals Nonlinear Aeroelastic System Identification Based on Neural Network

2018 ◽  
Vol 8 (10) ◽  
pp. 1916
Author(s):  
Bo Zhang ◽  
Jinglong Han ◽  
Haiwei Yun ◽  
Xiaomao Chen
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
System Identification ◽  
Control Surface ◽  
Identification Algorithm ◽  
Aeroelastic System ◽  
Structural Nonlinearity ◽  
Time Marching ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

Research of Fan System Identification Based on Improved Bayes Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 448-453
Author(s):  
Hai Tao Wang ◽  
Ze Zhang
Keyword(s):  
System Identification ◽  
Dynamic Characteristics ◽  
System Modeling ◽  
Estimation Algorithm ◽  
Parametric Model ◽  
Testing System ◽  
Identification Algorithm ◽  
Environment Protection ◽  
Parameter Estimation Algorithm ◽  
Bayes Algorithm

In every filed of natural science, more and more researchers attach importance to system quantitative analysis, control and prediction. In filed of automatic control, system identification is the extension of system dynamic characteristics testing. System modeling is the basis of system identification, non-parametric model can be obtained by means of dynamic characteristics testing, but parametric model must be established by means of parameter estimation algorithm, which is more prevalent than dynamic characteristics testing. Coal power plant produces more gas and dust, so how to control the fan system plays a very important role in environment protection. We must clarify the parameter of fan system before controlling it. The traditional Bayes identification algorithm is used widely in research and industry, and the effect is relatively good. The paper induces the concept of loss function based on traditional Bayes identification algorithm, and proposes an improved Bayes identification algorithm, which can be applied to fan system identification successfully.

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