scholarly journals Implementation of a Fuzzy Control System for Two-Wheeled Inverted Pendulum Robot based on Artificial Neural Network

2013 ◽  
Vol 17 (1) ◽  
pp. 8-14 ◽  
Author(s):  
Geon-Wu Jeong ◽  
Young-Kiu Choi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Inverted Pendulum ◽  
Fuzzy Control System ◽  
Wheeled Inverted Pendulum ◽  
Artificial Neural

scholarly journals A Unified Control Strategy of Distributed Generation for Grid-Connected and Islanded Operation Conditions Using an Artificial Neural Network

2021 ◽  
Vol 13 (11) ◽  
pp. 6388
Author(s):  
Karim M. El-Sharawy ◽  
Hatem Y. Diab ◽  
Mahmoud O. Abdelsalam ◽  
Mostafa I. Marei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Distributed Generation ◽  
Control Strategy ◽  
Current Control ◽  
Operating Conditions ◽  
Pi Controllers ◽  
Artificial Neural ◽  
The Stability

This article presents a control strategy that enables both islanded and grid-tied operations of a three-phase inverter in distributed generation. This distributed generation (DG) is based on a dramatically evolved direct current (DC) source. A unified control strategy is introduced to operate the interface in either the isolated or grid-connected modes. The proposed control system is based on the instantaneous tracking of the active power flow in order to achieve current control in the grid-connected mode and retain the stability of the frequency using phase-locked loop (PLL) circuits at the point of common coupling (PCC), in addition to managing the reactive power supplied to the grid. On the other side, the proposed control system is also based on the instantaneous tracking of the voltage to achieve the voltage control in the standalone mode and retain the stability of the frequency by using another circuit including a special equation (wt = 2πft, f = 50 Hz). This utilization provides the ability to obtain voltage stability across the critical load. One benefit of the proposed control strategy is that the design of the controller remains unconverted for other operating conditions. The simulation results are added to evaluate the performance of the proposed control technology using a different method; the first method used basic proportional integration (PI) controllers, and the second method used adaptive proportional integration (PI) controllers, i.e., an Artificial Neural Network (ANN).

scholarly journals Experimental implementation of artificial neural network-based active vibration control & chatter suppression

2021 ◽  
Author(s):  
Yong Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Control System ◽  
Vibration Control ◽  
Control Strategies ◽  
Active Vibration Control ◽  
Chatter Suppression ◽  
Active Vibration ◽  
Artificial Neural

Vibration control strategies strive to reduce the effect of harmful vibrations such as machining chatter. In general, these strategies are classified as passive or active. While passive vibration control techniques are generally less complex, there is a limit to their effectiveness. Active vibration control strategies, which work by providing an additional energy supply to vibration systems, on the other hand, require more complex algorithms but can be very effective. In this work, a novel artificial neural network-based active vibration control system has been developed. The developed system can detect the sinusoidal vibration component with the highest power and suppress it in one control cycle, and in subsequent cycles, sinusoidal signals with the next highest power will be suppressed. With artificial neural networks trained to cover enough frequency and amplitude ranges, most of the original vibration can be suppressed. The efficiency of the proposed methodology has been verified experimentally in the vibration control of a cantilever beam. Artificial neural networks can be trained automatically for updated time delays in the system when necessary. Experimental results show that the developed active vibration control system is real time, adaptable, robust, effective and easy to be implemented. Finally, an experimental setup of chatter suppression for a lathe has been successfully implemented, and the successful techniques used in the previous artificial neural network-based active vibration control system have been utilized for active chatter suppression in turning.

Neural Controlling of Remotely Operated Underwater Vehicle

2011 ◽  
Vol 180 ◽  
pp. 168-174 ◽  
Author(s):  
Andrzej Żak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Control System ◽  
Underwater Vehicle ◽  
Mathematical Basis ◽  
Dynamical Object ◽  
Artificial Neural

The main aim of paper is to introduce the results of research concentrated on controlling remotely operated underwater vehicle using artificial neural networks. Firstly the mathematical basis of neural network using to control dynamical object were introduced. Next the proposed control system which is using technology of artificial neural network was presented. At the end the example results of research on stabilizing movements’ parameters of underwater vehicle using ROV simulator were presented. The paper is finished by summary which include conclusions derive from results of research.

scholarly journals Designing and Analyzing the PID and Fuzzy Control System for an Inverted Pendulum

2020 ◽  
Author(s):  
Armin Masoumian ◽  
Pezhman Kazemi ◽  
Mohammad Chehreghani Montazer ◽  
Hatem A. Rashwan ◽  
Domenec Puig Valls
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Inverted Pendulum ◽  
Fuzzy Control System

Assembler Encoding versus Connectivity Matrix Encoding in the Inverted Pendulum Problem with a Hidden State

2010 ◽  
Vol 164 ◽  
pp. 233-238
Author(s):  
Tomasz Praczyk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problem ◽  
Inverted Pendulum ◽  
Connectivity Matrix ◽  
Predator Prey ◽  
Matrix Encoding ◽  
Artificial Neural ◽  
Encoding Method ◽  
Network Encoding

Assembler Encoding is Artificial Neural Network encoding method. To date Assembler Encoding has been tested in the optimization problem and in the so-called predator-prey problem. The paper reports experiments in a next test problem, i.e. in the inverted pendulum problem. During the experiments two direct encodings were also tested in order to compare Assembler Encoding with other Artificial Neural Network encoding methods.

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