Friction Compensation in the Inverted Pendulum Controller by Means of a Neural Network

2020 ◽  
Vol 22 (4) ◽  
pp. 875-884
Author(s):  
Marek Balcerzak
Keyword(s):  
Neural Network ◽  
Inverted Pendulum ◽  
Friction Model ◽  
The Novel ◽  
Practical Procedure ◽  
The Neural Network ◽  
Wide Range ◽  
Novel Method ◽  
Data Acquisition And Processing ◽  
Friction Modelling

AbstractThis paper presents an experimental confirmation of the novel method of friction modelling and compensation. The method has been applied to an inverted pendulum control system. The practical procedure of data acquisition and processing has been described. Training of the neural network friction model has been covered. Application of the obtained model has been presented. The main asset of the presented model is its correctness in a wide range of relative velocities. Moreover, the model is relatively easy to build.

The Novel Method of Structural Health Monitoring Using FEM and Neural Networks

2005 ◽  
Vol 475-479 ◽  
pp. 2099-2102 ◽  
Author(s):  
Shijie Zheng ◽  
Hong Tao Wang ◽  
Lifeng Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Composite Beam ◽  
Epoxy Composite ◽  
The Novel ◽  
Element Analysis ◽  
The Neural Network ◽  
The Finite Element Analysis ◽  
Novel Method ◽  
Modal Values

In this paper, a new method of combining computational mechanics and neural networks for prediction of composite beam delamination is proposed. One beam with delamination, as well as a ‘healthy’ beam with no delamination, had a four-ply symmetric carbon/epoxy composite design, were fabricated simultaneously. The delamination was assumed at different location of the beam, and then the finite element analysis was performed and the modal frequencies of the composite beam were obtained, which were used to train the neural network. The piezoelectric patch was attached to the top of the composite beam to measure its modal frequencies. A feedforward backpropagation neural network was designed, trained, and used to predict the delamination location using the experimental modal values as inputs. The experimental results demonstrate that the predicted delamination location and size error is small.

scholarly journals Diagnosis of the Accuracy of the Vehicle Scale Using Neural Network

2019 ◽  
Vol 19 (1) ◽  
pp. 14-19
Author(s):  
Tomáš Kliment ◽  
Jaromír Markovič ◽  
Dušan Šmigura ◽  
Peter Adam
Keyword(s):  
Neural Network ◽  
The Novel ◽  
Legal Metrology ◽  
Network Algorithm ◽  
The Neural Network ◽  
Trained Classifier ◽  
Neural Network Algorithm ◽  
Novel Method

Abstract The article describes a method for diagnosing the accuracy of the vehicle scale without using standard weights. The novel method defines the possibility to estimate whether the scale would pass the test for error of indication in the next verification or not, only by using the results from simple tests with load of estimated weight and appropriate classifier. The method is primarily developed for users of these scales. Created classifier is based on the neural network algorithm. The neural network was trained with data from verifications, which are provided by Slovak Legal Metrology. Well trained classifier can provide not only information whether the scale will potentially pass the mentioned test or not, but reliability which is associated with this result as well. In this way, the user has valuable information about the scale in the period between the verifications.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

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.

scholarly journals A Novel Method for Tuning PID Controller

2013 ◽  
Vol 6 (1) ◽  
pp. 62-74
Author(s):  
Abidaoun H. shallal ◽  
Rawaa A. Karim ◽  
Osama Y. Al-Rawi
Keyword(s):  
Computer Technology ◽  
Pid Control ◽  
Pid Controller ◽  
Control Algorithm ◽  
The Novel ◽  
Operation Conditions ◽  
Wide Range ◽  
Novel Method ◽  
Mechanical Performances ◽  
Dc Servomotor

Proportional integral derivative (PID) control is the most commonly used  control algorithm in the industry today. PID controller popularity can be attributed to the  controller’s effectiveness in a wide range of operation conditions, its functional simplicity, and the ease with which engineers can implement it using current computer technology . In this paper,the Dc servomotor model is chosen according to his good electrical and mechanical performances more than other Dc motor models , discuss the novel method for  tuning PID controller and comparison with Ziegler - Nichols method from through parameters of transient response of any system which uses PID compensator

A Graph Grammar Approach to Generate Neural Network Topologies

2007 ◽  
Author(s):  
Chaitanya Vempati ◽  
Matthew I. Campbell
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Process Modeling ◽  
Graph Transformation ◽  
Graph Grammar ◽  
Transformation Rules ◽  
The Neural Network ◽  
Network Topologies ◽  
Novel Method ◽  
Defining Graph

Neural networks are increasingly becoming a useful and popular choice for process modeling. The success of neural networks in effectively modeling a certain problem depends on the topology of the neural network. Generating topologies manually relies on previous neural network experience and is tedious and difficult. Hence there is a rising need for a method that generates neural network topologies for different problems automatically. Current methods such as growing, pruning and using genetic algorithms for this task are very complicated and do not explore all the possible topologies. This paper presents a novel method of automatically generating neural networks using a graph grammar. The approach involves representing the neural network as a graph and defining graph transformation rules to generate the topologies. The approach is simple, efficient and has the ability to create topologies of varying complexity. Two example problems are presented to demonstrate the power of our approach.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

scholarly journals Evaluation of the Dreicer runaway generation rate in the presence of high- impurities using a neural network

2019 ◽  
Vol 85 (6) ◽  
Author(s):  
L. Hesslow ◽  
L. Unnerfelt ◽  
O. Vallhagen ◽  
O. Embreus ◽  
M. Hoppe ◽  
...  
Keyword(s):  
Neural Network ◽  
Runaway Electron ◽  
The Self ◽  
Generation Rate ◽  
Integrated Modelling ◽  
Plasma Parameters ◽  
The Neural Network ◽  
Wide Range ◽  
Self Consistent ◽  
Computationally Expensive

Integrated modelling of electron runaway requires computationally expensive kinetic models that are self-consistently coupled to the evolution of the background plasma parameters. The computational expense can be reduced by using parameterized runaway generation rates rather than solving the full kinetic problem. However, currently available generation rates neglect several important effects; in particular, they are not valid in the presence of partially ionized impurities. In this work, we construct a multilayer neural network for the Dreicer runaway generation rate which is trained on data obtained from kinetic simulations performed for a wide range of plasma parameters and impurities. The neural network accurately reproduces the Dreicer runaway generation rate obtained by the kinetic solver. By implementing it in a fluid runaway-electron modelling tool, we show that the improved generation rates lead to significant differences in the self-consistent runaway dynamics as compared to the results using the previously available formulas for the runaway generation rate.

scholarly journals Pesquisa sobre empresas multinacionais brasileiras: análises descritivas e preditivas

Internext ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. 64
Author(s):  
Mario Henrique Ogasavara ◽  
Gilmar Masiero ◽  
Marcio De Oliveira Mota ◽  
Lucas Souza
Keyword(s):  
Neural Network ◽  
Citation Analysis ◽  
Multinational Enterprises ◽  
Descriptive Analysis ◽  
Predictive Analysis ◽  
The Novel ◽  
Neural Network Approach ◽  
Novel Approach ◽  
The Neural Network ◽  
Eclectic Paradigm

<p><em>T</em>his study attempts to review recent research on the internationalization of Brazilian multinational enterprises (I-BMNEs) based on an analysis of the 174 published articles that have appeared in international and Brazilian academic journals, books, and conference proceedings. The descriptive analysis seeks to undertake a citation analysis as well as to provide a typology of the leading researchers and school affiliations, the predominating theoretical and methodological approaches. This paper also proposes a predictive analysis based on a novel approach of neural network in order to classify features of a manuscript and predict the fit of its publication. We find that the research on I-BMNEs is driven by a small number of leading institutions and researchers which utilize case studies as their research method and have the Uppsala and Eclectic Paradigm models as theoretical framework. The citation analysis shows that authors of foreigner origin are cited from journal publications or translated books. The novel technique and design of the neural network approach was modeled to fit for bibliometric studies and the outcomes of the predictive analysis were able to classify correctly 56.25% of the manuscripts. We conclude by providing a set of recommendations to advance the research on I-BMNEs.</p>

scholarly journals Development of a generative adversarial neural network for identification of potential HIV-1 inhibitors by deep learning methods

Informatics ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 7-17
Author(s):  
G. I. Nikolaev ◽  
N. A. Shuldov ◽  
A. I. Anishenko, ◽  
A. V. Tuzikov ◽  
A. M. Andrianov
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Rational Design ◽  
Binding Free Energy ◽  
Viral Envelope ◽  
Training Dataset ◽  
Learning Methods ◽  
The Neural Network ◽  
Wide Range ◽  
Hiv 1

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block the region of the viral envelope protein gp120 critical for the virus binding to cellular receptor CD4 was developed using deep learning methods. The research were carried out to create the  architecture of the neural network, to form  virtual compound library of potential anti-HIV-1 agents for training the neural network, to make  molecular docking of all compounds from this library with gp120, to  calculate the values of binding free energy, to generate molecular fingerprints for chemical compounds from the training dataset. The training the neural network was implemented followed by estimation of the learning outcomes and work of the autoencoder.  The validation of the neural network on a wide range of compounds from the ZINC database was carried out. The use of the neural network in combination with virtual screening of chemical databases was shown to form a productive platform for identifying the basic structures promising for the design of novel antiviral drugs that inhibit the early stages of HIV infection.

scholarly journals CNN-Based Illumination Estimation with Semantic Information

2020 ◽  
Vol 10 (14) ◽  
pp. 4806 ◽  
Author(s):  
Ho-Hyoung Choi ◽  
Hyun-Soo Kang ◽  
Byoung-Ju Yun
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Semantic Information ◽  
Color Constancy ◽  
Noisy Data ◽  
Image Data ◽  
Input Image ◽  
The Novel ◽  
Image Patches ◽  
Wide Range

For more than a decade, both academia and industry have focused attention on the computer vision and in particular the computational color constancy (CVCC). The CVCC is used as a fundamental preprocessing task in a wide range of computer vision applications. While our human visual system (HVS) has the innate ability to perceive constant surface colors of objects under varying illumination spectra, the computer vision is facing the color constancy challenge in nature. Accordingly, this article proposes novel convolutional neural network (CNN) architecture based on the residual neural network which consists of pre-activation, atrous or dilated convolution and batch normalization. The proposed network can automatically decide what to learn from input image data and how to pool without supervision. When receiving input image data, the proposed network crops each image into image patches prior to training. Once the network begins learning, local semantic information is automatically extracted from the image patches and fed to its novel pooling layer. As a result of the semantic pooling, a weighted map or a mask is generated. Simultaneously, the extracted information is estimated and combined to form global information during training. The use of the novel pooling layer enables the proposed network to distinguish between useful data and noisy data, and thus efficiently remove noisy data during learning and evaluating. The main contribution of the proposed network is taking CVCC to higher accuracy and efficiency by adopting the novel pooling method. The experimental results demonstrate that the proposed network outperforms its conventional counterparts in estimation accuracy.

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