Dynamic Modeling and Control Analysis of Industrial Electromechanical Servo Positioning System Using Machine Learning Technique

2020 ◽  
Vol 49 (4) ◽  
pp. 20200159
Author(s):  
S. Thangavel ◽  
C. Maheswari ◽  
E. B. Priyanka
Keyword(s):  
Machine Learning ◽  
Dynamic Modeling ◽  
Control Analysis ◽  
Positioning System ◽  
Machine Learning Technique ◽  
Modeling And Control ◽  
Learning Technique ◽  
And Control

Dynamic modeling and control analysis of a methanol autothermal reforming and PEM fuel cell power system

2017 ◽  
Vol 208 ◽  
pp. 703-718 ◽  
Author(s):  
Dimitris Ipsakis ◽  
Martha Ouzounidou ◽  
Simira Papadopoulou ◽  
Panos Seferlis ◽  
Spyros Voutetakis
Keyword(s):  
Fuel Cell ◽  
Power System ◽  
Dynamic Modeling ◽  
Pem Fuel Cell ◽  
Autothermal Reforming ◽  
Control Analysis ◽  
Modeling And Control ◽  
And Control

scholarly journals IOT-HML: A Hybrid Machine Learning Technique for IoT Enabled Industrial Monitoring and Control System

2021 ◽  
Author(s):  
Kota P.N. ◽  
Chandak A.S. ◽  
Patil B.P.
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Information Security ◽  
Monitoring And Control ◽  
Machine Learning Technique ◽  
Monitoring And Control System ◽  
Learning Technique ◽  
Industrial Monitoring ◽  
Hybrid Machine ◽  
And Control

Abstract Industry 4.0 makes manufacturers more vulnerable to current challenges and makes it easier to adapt to market changes. This will increase the speed of innovation, make it more customer-oriented and lead to faster design processes. It is essential to focus on monitoring and controlling the production system before complex accidents occur. Moreover, an industrial control system facing information security problems in recent times because of the nature of IoT which affects the evaluation of abnormal predication. To overcome above research gaps, we shift to industrial 4.0 which combine IoT and mechanism learning for industrial monitor and manage. We propose a hybrid machine learning technique for IoT enabled industrial monitoring and control system (IoT-HML). Here, we concentrate both information security issues with accurate monitoring and control system. The first section of proposed IoT-HML system is to introduce the cat induced wheel optimization (IWO) algorithm for cluster formation. The process consists of clustering and cluster head (CH) selection. The source node forward information to destination through CH only which avoids the unwanted data loss and improve the security, because the information travel through trusted path. For route selection process, we utilize the cuckoo search algorithm to compute the optimal best path among multiples. In second section, we illustrate a coach and player learned neural network (CP-LNN) for monitoring the industrial and prevent from accidents by basic control strategies. Finally, the proposed IoT-HML system can evaluate with different set of data’s to prove the effectiveness.

scholarly journals Dynamics and control analysis of a single flexible link robot with translational joints

2020 ◽  
Vol 3 (4) ◽  
pp. first
Author(s):  
Bien Duong Xuan
Keyword(s):  
Dynamic Modeling ◽  
Control Analysis ◽  
Flexible Link ◽  
Lagrange Equations ◽  
Flexible Robots ◽  
Modeling And Control ◽  
Dynamics And Control ◽  
Translational Joint ◽  
Joint Variable ◽  
And Control

Modern design always aims at reducing mass, simplifying the structure, and reducing the energy consumption of the system especially in robotics. These targets could lead to lowing cost of the material and increasing the operating capacity. The priority direction in robot design is optimal structures with longer lengths of the links, smaller and thinner links, more economical still warranting ability to work. However, all of these structures such as flexible robots are reducing rigidity and motion accuracy because of the effect of elastic deformations. Therefore, taking the effects of elastic factor into consideration is absolutely necessary for kinematic, dynamic modeling, analyzing, and controlling flexible robots. Because of the complexity of modeling and controlling flexible robots, the single-link and two-link flexible robots with only rotational joints are mainly mentioned and studied by most researchers. It is easy to realize that combining the different types of joints of flexible robots can extend their applications, flexibility, and types of structure. However, the models consisting of rotational and translational joints will make the kinematic, dynamic modeling, and control becomes more complex than models that have only rotational joints. This study focuses on the dynamics model and optimal controller based on genetic algorithms (GA) for a single flexible link robot (FLR) with a rigid translational joint. The motion equations of the FLR are built based on the Finite Element Method (FEM) and Lagrange Equations (LE). The difference between flexible manipulators that have only rotational joints and others with the translational joint is presented through boundary conditions. A PID controller is designed with parameters that are optimized by the GA algorithm. The cost function is established based on errors signal of translational joint, elastic displacements of the End-Point (EP) of the FLR. Simulation results show that the errors of the joint variable, the elastic displacements (ED) are destructed in a short time when the system is controlled following the reference point. The results of this study can be basic to research other flexible robots with more joint or combine joint styles.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection
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