scholarly journals PD-SVM Integrated Controller for Robotic Manipulator Tracking Control

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Neha Kapoor ◽  
Jyoti Ohri
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
System Performance ◽  
Tracking Control ◽  
Pid Controller ◽  
Robotic Manipulator ◽  
Support Vector ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Noise Disturbances

Highly precise tracking of a robotic manipulator in presence of uncertainties like noise, disturbances, and friction has been addressed in this particular paper. An integrated proportional derivative and support vector machine (SVMPD) controller has been proposed for manipulator tracking. To illustrate the efficiency of the proposed controller, simulations have been done on a 2-DOF manipulator system. Performance of the proposed controller has been checked and verified with respect to to a simple PID controller and the radial bias neural network proportional integral derivative (RBNNPD) controller. It has been proved that the proposed controller can achieve better tracking performance as compared to other controllers as the range of errors is less and the time taken by the controller has reduced up to 14 times as compared to RBNN.

Motion modeling of a non-holonomic wheeled mobile robot based on trajectory tracking control

2020 ◽  
Vol 44 (2) ◽  
pp. 228-233
Author(s):  
Xuefeng Han ◽  
Mingda Ge ◽  
Jicheng Cui ◽  
Hao Wang ◽  
Wei Zhuang
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Kinematic Model ◽  
Wheeled Mobile Robot ◽  
Proportional Integral Derivative ◽  
Trajectory Tracking Control ◽  
Proportional Integral

Trajectory tracking is a problem of emphasis for the mobile robot. In this study, a coordinate transformation method was used to build a kinematic model of the wheeled mobile robot. A traditional proportional-integral-derivative control method was researched and improved by combining it with a neural network. A neural network proportional-integral-derivative trajectory tracking control method was thus designed, and a simulation experiment was performed using Simulink. The results show that in circular trajectory tracking control, the maximum errors of the X axis, Y axis, and θ were approximately 2.1 m, 2.3 m, and 0.4 rad, respectively, and that the system remained stable after running for 10 s. In straight-line trajectory tracking control, the maximum errors of the X axis, Y axis, and θ were approximately −0.8 m, 1.3 m, and 0.3 rad, respectively, and the system remained stable after running for 8 s. The error was relatively small, and the effect of trajectory tracking control was good. The studied method had good performance in terms of wheeled mobile robot trajectory tracking control and is worthy of further promotion and application.

Effectiveness of the Adaptive PID Controller for Traction Motor in Hybrid Electric Vehicles

2014 ◽  
Vol 699 ◽  
pp. 765-769
Author(s):  
Mohd Sabirin Rahmat ◽  
Fauzi Ahmad ◽  
Ahmad Kamal Mat Yamin ◽  
Noreffendy Tamaldin ◽  
Vimal Rau Aparow
Keyword(s):  
Electric Vehicles ◽  
Tracking Control ◽  
Pid Controller ◽  
Hybrid Electric Vehicles ◽  
Permanent Magnet Synchronous Motor ◽  
Sine Wave ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Hybrid Electric ◽  
Detailed Derivation

This paper presents a detailed derivation of permanent magnet synchronous motor (PMSM), which can be used as the prime mover for simulation of hybrid electric vehicles (HEV). A torque tracking control for the motor is developed based on the adaptive proportional-integral-derivative (APID) controller .The effectiveness of the controller has been mathematically compared with the proportional-integral-derivative (PID) controller using various input functions such as step, saw tooth, sine wave and square wave. The results show that the APID-based control is effective in tracking the desired torque with good response and is able to adapt with the desired input as compared to the PID controller. This finding supports the selection of the APID for the toque tracking control of hybrid electric vehicles.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

2019 ◽  
Vol 13 ◽  
Author(s):  
Niha Kamal Basha ◽  
Aisha Banu Wahab
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Monitoring System ◽  
Single Channel ◽  
Confusion Matrix ◽  
Sudden Change ◽  
Automatic Detection ◽  
Support Vector ◽  
Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

scholarly journals SEOpinion: Summarization and Exploration of Opinion from E-Commerce Websites

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 636
Author(s):  
Alhassan Mabrouk ◽  
Rebeca P. Díaz Redondo ◽  
Mohammed Kayed
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Cognitive Processing ◽  
Support Vector ◽  
Processing Capacity ◽  
Customer Reviews ◽  
Aspect Extraction ◽  
Descriptive Information ◽  
Opinion Summarization ◽  
Second Phases

Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research authors have designed opinion summarization systems based on customer reviews. They ignored the template information provided by manufacturers, although its descriptive information has the most useful product characteristics and texts are linguistically correct, unlike reviews. Therefore, this paper proposes a methodology coined as SEOpinion (summarization and exploration of opinions) to summarize aspects and spot opinion(s) regarding them using a combination of template information with customer reviews in two main phases. First, the hierarchical aspect extraction (HAE) phase creates a hierarchy of aspects from the template. Subsequently, the hierarchical aspect-based opinion summarization (HAOS) phase enriches this hierarchy with customers’ opinions to be shown to other potential buyers. To test the feasibility of using deep learning-based BERT techniques with our approach, we created a corpus by gathering information from the top five EC websites for laptops. The experimental results showed that recurrent neural network (RNN) achieved better results (77.4% and 82.6% in terms of F1-measure for the first and second phases, respectively) than the convolutional neural network (CNN) and the support vector machine (SVM) technique.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

PIDNN control for Vernier-gimballing magnetically suspended flywheel under nonlinear change of stiffness and disturbance

2020 ◽  
pp. 095965182097757
Author(s):  
Jiqiang Tang ◽  
Mengyue Ning ◽  
Xu Cui ◽  
Tongkun Wei ◽  
Xiaofeng Zhao
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Magnetic Force ◽  
Magnetic Bearing ◽  
Network Control ◽  
Gradient Descent Method ◽  
Neural Network Control ◽  
Proportional Integral Derivative ◽  
Tracking Accuracy ◽  
Proportional Integral

Vernier-gimballing magnetically suspended flywheel is often used for attitude control and interference suppression of spacecrafts. Due to the special structure of the conical magnetic bearing, the radial component generated by the axial magnetic force and the change of the magnetic air gap will cause the nonlinearity of stiffness and disturbance. That will lead to not only poor stability of the suspension control system but also unsatisfactory tracking accuracy of the rotor position. To solve the nonlinear problem of the system, this article proposes a proportional–integral–derivative neural network control scheme. First, the rotor model considering the nonlinear variation of disturbance and stiffness parameters is established. Then, the weight of neural network is adjusted by the gradient descent method online to ensure the accurate output of magnetic force. Finally, the convergence analysis is carried out based on the Lyapunov stability theory. Compared with the general proportional–integral–derivative control and the radial basis function neural network control, the simulation results demonstrate that the proposed method has the highest tracking accuracy and excellent performance in improving stability. The experimental results prove the correctness of the theoretical analysis and the validity of the proposed method.

scholarly journals Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 639
Author(s):  
Chen Ma ◽  
Haifei Dang ◽  
Jun Du ◽  
Pengfei He ◽  
Minbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Manufacturing Process ◽  
Support Vector ◽  
Defect Classification ◽  
Droplet Deposition ◽  
Network Support ◽  
Visual Sensing ◽  
Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

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