scholarly journals Parameter Identification of Tractor-Semitrailer Model under Steering and Braking

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Yiming Li ◽  
Qin Shi ◽  
Duoyang Qiu
Keyword(s):  
Genetic Algorithm ◽  
Field Test ◽  
Test Data ◽  
Performance Testing ◽  
Roll Angle ◽  
Lookup Table ◽  
The Real ◽  
Yaw Rate ◽  
Roll Rate ◽  
Good Agreement

This paper describes a valuable linear yaw-roll tractor-semitrailer (TST) model with five-degree-of-freedom (DOFs) for control algorithm development when steering and braking. The key parameters, roll stiffness, axle cornering stiffness, and fifth-wheel stiffness, are identified by the genetic algorithm (GA) and multistage genetic algorithm (MGA) based on TruckSim outputs to increase the accuracy of the model. Thus, the key parameters of the simplified model can be modified according to the real-time vehicle states by online lookup table and interpolation. The TruckSim vehicle model is built referring to the real tractor (JAC-HFC4251P1K7E33ZTF6×2) and semitrailer (Luyue LHX9406) used in the field test later. The validation of the linear yaw-roll model of a tractor-semitrailer using field test data is presented in this paper. The field test in the performance testing ground is detailed, and the test data of roll angle, roll rate, and yaw rate are compared with the outputs of the model with maps of the key parameters. The results indicate that the error of the tractor’s roll angle and semitrailer’s roll angle between model data and test data is 1.13% and 1.24%, respectively. The roll rate and yaw rate of the tractor and semitrailer are also in good agreement.

scholarly journals Generating Test Data for Path Coverage Based on Genetic Algorithm

2020 ◽  
Vol 719 ◽  
pp. 012070
Author(s):  
Shuping Fan ◽  
Baoying Ma ◽  
Nianmin Yao ◽  
Yan Zhang ◽  
Chunyan Xia ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Path Coverage

Predicting Seepage of Earth Dams Using Neural Network and Genetic Algorithm

2011 ◽  
Vol 403-408 ◽  
pp. 3081-3085 ◽  
Author(s):  
Xin Ying Miao ◽  
Jin Kui Chu ◽  
Jing Qiao ◽  
Ling Han Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Field Data ◽  
Back Propagation ◽  
Earth Dam ◽  
Earth Dams ◽  
Gradient Descent Algorithm ◽  
Levenberg Marquardt ◽  
Lm Algorithm ◽  
Good Agreement

Measurements of seepage are fundamental for earth dam surveillance. However, it is difficult to establish an effective and practical dam seepage prediction model due to the nonlinearity between seepage and its influencing factors. Genetic Algorithm for Levenberg-Marquardt(GA-LM), a new neural network(NN) model has been developed for predicting the seepage of an earth dam in China using 381 databases of field data (of which 366 in 2008 were used for training and 15 in 2009 for testing). Genetic algorithm(GA) is an ecological system algorithm, which was adopted to optimize the NN structure. Levenberg-Marquardt (LM) algorithm was originally designed to serve as an intermediate optimization algorithm between the Gauss-Newton(GN) method and the gradient descent algorithm, which was used to train NN. The predicted seepage values using GA-LM model are in good agreement with the field data. It is demonstrated here that the model is capable of predicting the seepage of earth dams accurately. The performance of GA-LM has been compared with that of conventional Back-Propagation(BP) algorithm and LM algorithm with trial-and-error approach. The comparison indicates that the GA-LM model can offer stronger and better performance than conventional NNs when used as a quick interpolation and extrapolation tool.

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

A Stochastic Theory for Nonlinear Ship Rolling in irregular Seas

1982 ◽  
Vol 26 (04) ◽  
pp. 229-245 ◽  
Author(s):  
J. B. Roberts
Keyword(s):  
Markov Processes ◽  
Digital Simulation ◽  
Stochastic Theory ◽  
Simple Expression ◽  
Roll Angle ◽  
Rolling Motion ◽  
Approximate Theory ◽  
Averaging Techniques ◽  
Good Agreement ◽  
Ship Rolling

By a combination of averaging techniques with the theory of Markov processes, an approximate theory is developed for the rolling motion of a ship in beam waves. A simple expression is obtained for the distribution of the roll angle, and is tested by a comparison with a set of digital simulation estimates due to Dalzell. Good agreement is obtained over a realistic range of damping values.

A Processing Method for Combined Fatigue Accelerated Test Data

2017 ◽  
Author(s):  
Songwang Zheng ◽  
Cao Chen ◽  
Lei Han ◽  
Xiaoyong Zhang ◽  
Xiaojun Yan
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Turbine Blades ◽  
Processing Method ◽  
Test Time ◽  
Accelerated Test ◽  
The Real ◽  
Data Processing Method ◽  
Stress Levels ◽  
Vibration Stress

To carry out combined low and high cycle fatigue (CCF) test on turbine blades in a bench environment, it is imperative to simulate the vibration loads of turbine blades in the field. Due to the low vibration stress of turbine blades in the working state, the test time will be very long if the test vibration stress is equal to the real vibration stress in working state. Therefore, an accelerated test will be used when the test life reach the target value (typically 107). During the accelerated test, each blade is tested at two or more times than the real vibration stress. That means some specimens are tested under two vibration stress levels. In this case, a reasonable data processing method becomes very important. For this reason, a data processing method for the CCF accelerated test is proposed in this paper. These test data are iterated on the basis of S-N curve. Finally, ten real turbine blades are tested in a bench environment, one of them is tested under two vibration stress levels. The test data is processed using the method proposed above to obtain the unaccelerated life data.

Estimation of Track Irregularity Based on Genetic Algorithm and Unscented Kalman Filtering

2012 ◽  
Author(s):  
Hongmei Shi ◽  
Zujun Yu
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filtering ◽  
High Speed ◽  
Measurement Data ◽  
Estimation Method ◽  
Coupling Model ◽  
Dynamic Responses ◽  
The Real ◽  
True Value ◽  
Track Irregularity

Track irregularity is the main excitation source of wheel-track interaction. Due to the difference of speed, axle load and suspension parameters between track inspection train and the operating trains, the data acquired from the inspection car cannot completely reflect the real status of track irregularity when the operating trains go through the rail. In this paper, an estimation method of track irregularity is proposed using genetic algorithm and Unscented Kalman Filtering. Firstly, a vehicle-track vertical coupling model is established, in which the high-speed vehicle is assumed as a rigid body with two layers of spring and damping system and the track is viewed as an elastic system with three layers. Then, the static track irregularity is estimated by genetic algorithm using the vibration data of vehicle and dynamic track irregularity which are acquired from the inspection car. And the dynamic responses of vehicle and track can be solved if the static track irregularity is known. So combining with vehicle track coupling model of different operating train, the potential dynamic track irregularity is solved by simulation, which the operating train could goes through. To get a better estimation result, Unscented Kalman Filtering (UKF) algorithm is employed to optimize the dynamic responses of rail using measurement data of vehicle vibration. The simulation results show that the estimated static track irregularity and the vibration responses of vehicle track system can go well with the true value. It can be realized to estimate the real rail status when different trains go through the rail by this method.

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