Predictive S Control of AUV Based on Model of Support Vector Machine

2011 ◽  
Vol 340 ◽  
pp. 421-428
Author(s):  
Guo Cheng Zhang ◽  
Lei Zhang ◽  
Lei Wan ◽  
Ji Qing Li
Keyword(s):  
Support Vector Machine ◽  
Motion Control ◽  
Error Function ◽  
Time Lag ◽  
Good Control ◽  
Working Environment ◽  
Support Vector ◽  
Control Parameters ◽  
Fine Property ◽  
Dynamic Performances

Because of nonlinear dynamic performances and uncertain working environment, precise motion control of Automatic Underwater Vehicle (AUV) has always been a problem. For the time lag and nonlinearity of AUV, a new method called Predictive S Control (PSC) based on model of Support Vector Machine (SVM) is presented. Firstly, in establishing the model of SVM, the fine property of approaching to the nonlinear model is utilized to solve the predictive problem. Then, the control parameters of S controller are optimized by constructing the error function. At last the motion control of AUV is realized. It has been proved that this method is feasible and effective. Using this method, a good control result is obtained in the simulation.

A New Method for Fault Detection of Rotating Machines in Motion Control Applications Using PROFIdrive Information and Support Vector Machine Classifier

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Andre Luis Dias ◽  
Afonso Celso Turcato ◽  
Guilherme Serpa Sestito ◽  
Murilo Silveira Rocha ◽  
Dennis Brandão ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Motion Control ◽  
Diagnostic System ◽  
Operating Conditions ◽  
Support Vector ◽  
Electric Motors ◽  
Rotating Machines ◽  
Worst Case ◽  
Control Applications ◽  
Mechanical Faults

Abstract Electric motors are widely used in the industry. Several studies have proposed methods to detect anomalies in their operation, but always using sensors dedicated to this purpose. In this sense, this work aims to fill gaps in related works presenting a method for the detection of faults in rotating machines driven by electric motors in motion control applications using PROFINET network and PROFIdrive profile. The proposed method does not require any additional or dedicated sensors to provide data to the diagnostic system. Instead, the proposed methodology is based on the analysis of data transmitted in the communication network, which already exists for control purposes. Support vector machine (SVM) is used as a classifier of five different mechanical faults. The results provide that the methodology is feasible and efficient under different machine operating conditions, achieving, in the worst case, 97.78% efficiency.

scholarly journals Research on Accuracy Analysis and Motion Control of Two-axis Non-magnetic Turntable Based on Ultrasonic Motor Journal

Mechanika ◽  
2020 ◽  
Vol 26 (3) ◽  
pp. 221-230
Author(s):  
Zhuo WANG ◽  
Xin-tong WANG ◽  
Tao WANG ◽  
Hong-wen MA
Keyword(s):  
Steady State ◽  
Motion Control ◽  
Dynamic Performance ◽  
Angular Position ◽  
Ultrasonic Motor ◽  
Working Environment ◽  
Control Parameters ◽  
Positioning Accuracy ◽  
Position Information ◽  
State Error

In the calibration process of the positioner, in order to obtain the accurate angular position information of each axis of the turntable, it is necessary to test the rotation accuracy of the turntable higher than the accuracy of the target gyroscope, in order to achieve the test and calibration of the positioner. Therefore, this paper uses the non-magnetic technology to carry out research on the pointing accuracy and motion control of the turntable. At the same time, the influence of grating installation error on precision detection is analysed. The influence of steady-state error of turntable on the motion accuracy of turntable is analysed. Finally, the influence of servo control parameters on the dynamic performance of turntable and the influence on steady-state error are analysed. The test of the corner positioning accuracy of the turntable is carried out. The positioning accuracy and motion control parameters of the two-axis precision non-magnetic turntable are obtained, and the PID adjustment is introduced to make the accuracy index of the non-magnetic turntable meet the requirements. The turntable can realize a non-magnetic working environment and can achieve the high precision required by the index under the driving of the ultrasonic motor.

Rule Extraction from Support Vector Machine and its Application to Hot-Dip Galvanizing

2013 ◽  
Vol 572 ◽  
pp. 300-303
Author(s):  
Jian Guo Wang ◽  
Bin Yang ◽  
Wen Xing Zhang ◽  
Bo Qin
Keyword(s):  
Support Vector Machine ◽  
Production Control ◽  
Raw Materials ◽  
Zinc Coating ◽  
Quantitative Relationship ◽  
Rule Extraction ◽  
Quality Parameters ◽  
Support Vector ◽  
Control Parameters ◽  
Input Variables

A new rule extraction algorithm based on convex hull for strip hot-dip galvanizing process monitoring is proposed in this paper. It overcomes the black-box problem of support vector machine. The zinc coating weight is used as the investigated subject. The sample datasets are trained by support vector machine rule extraction method, and the quantitative relationship can be obtained in the form of knowledge rules among input variables (such as the parameters of raw materials and control parameters of production) and output ones (the quality parameters), with which the production control parameters can be set and updated easily.

Support vector machine-based two-wheeled mobile robot motion control in a noisy environment

2008 ◽  
Vol 222 (7) ◽  
pp. 733-743 ◽  
Author(s):  
L Jiang ◽  
M Deng ◽  
A Inoue
Keyword(s):  
Support Vector Machine ◽  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Support Vector ◽  
Robot Motion ◽  
Control Parameters ◽  
Local Minima ◽  
Noisy Environment ◽  
Virtual Force ◽  
Control Scheme

In this paper, a support vector machine (SVM)-based control scheme of a two-wheeled mobile robot is proposed in a noisy environment. The noisy environment is defined as the measured data with uncertainty. The proposed control scheme can control the robot by consideration of local minima, where the controller is based on the Lyapunov function candidate and considers virtual force information. The SVM method is used for estimating the control parameters from the noisy environment. Four simulation results are presented to show the effectiveness of the proposed control scheme in the noisy environment, while the performance of a former method degrades significantly.

Explicit Maps of Acoustic Regimes of a Wind Instrument

2013 ◽  
Author(s):  
Samy Missoum ◽  
Christophe Vergez
Keyword(s):  
Support Vector Machine ◽  
Adaptive Sampling ◽  
Support Vector Machine Classifier ◽  
Sampling Scheme ◽  
Support Vector ◽  
Control Parameters ◽  
Wind Instrument ◽  
Sustained Oscillations ◽  
Static Regime ◽  
And Control

An approach to map the various acoustic regimes of a wind instument is presented. In this work, the regimes are first classified based on the occurence or the lack of sound. Physically, the production of a sound corresponds to the existence of self-sustained oscillations in the resonator of the instrument, whereas the lack of sound is associated with a stable static regime. Another classification based on the sound frequency is also investigated. The maps are created in a space consisting of design and control parameters. The boundaries of the maps are obtained explicitly in terms of the parameters using a support vector machine classifier as well as a dedicated adaptive sampling scheme. The approach is applied to a simplified clarinet model.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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