scholarly journals A model-free hull deformation measurement method with time delay compensation

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881069 ◽  
Author(s):  
Ying He ◽  
Xiafu Peng ◽  
Xiaoli Zhang ◽  
Xiaoqiang Hu

Estimation and compensation for hull deformation is an indispensable step for the ship to establish a unified space attitude. The existing hull deformation measurement methods are dependent on the pre-established deformation model, and an inaccurate deformation model will reduce the deformation estimation accuracy. To solve this problem, a hull deformation estimation method without deformation model is proposed in this article, which utilizes the neural network to fit the hull deformation. To train the neural network online, connection weights of the neural network are regarded as system state variables which can be estimated by the Unscented Kalman Filter. Simultaneously, considering the time delay problem of inertial data, a time delay compensation method based on the quaternion attitude matrix is proposed. The simulation results show that the proposed method can obtain high estimation accuracy without any deformation model even when the inertial data are asynchronous.

2015 ◽  
Vol 135 (7) ◽  
pp. 755-764 ◽  
Author(s):  
Shuhei Shimizu ◽  
Yoshiki Ohno ◽  
Takahiro Nozaki ◽  
Kouhei Ohnishi

Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.


Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.


2003 ◽  
Vol 125 (3) ◽  
pp. 451-454 ◽  
Author(s):  
Han G. Park ◽  
Michail Zak

We present a fault detection method called the gray-box. The term “gray-box” refers to the approach wherein a deterministic model of system, i.e., “white box,” is used to filter the data and generate a residual, while a stochastic model, i.e., “black-box” is used to describe the residual. The residual is described by a three-tier stochastic model. An auto-regressive process, and a time-delay feed-forward neural network describe the linear and nonlinear components of the residual, respectively. The last component, the noise, is characterized by its moments. Faults are detected by monitoring the parameters of the auto-regressive model, the weights of the neural network, and the moments of noise. This method is demonstrated on a simulated system of a gas turbine with time delay feedback actuator.


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