Illustration and Control of Non-Isolated Multi-Input DC - DC Bidirectional Converter for Electric Vehicles Using Fuzzy Logic controller

2020 ◽  
Author(s):  
Gyandeep Gurjar ◽  
D. K Yadav ◽  
Seema Agrawal
Keyword(s):  
Fuzzy Logic ◽  
Electric Vehicles ◽  
Fuzzy Logic Controller ◽  
Bidirectional Converter ◽  
And Control

Fuzzy Logic in Hole Drilling of Composites

1994 ◽  
Author(s):  
Aditya Thadani ◽  
Athamaram H. Soni
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Thrust Force ◽  
Cutting Speed ◽  
Research Data ◽  
Theoretical Research ◽  
Hole Drilling ◽  
Tool Geometry ◽  
Drilling Process ◽  
And Control

Abstract Experimental and theoretical research data was utilized in building a Fuzzy Logic Controller model applied to simulate the drilling process of composite materials. The objective is to have a better understanding and control of delamination of composites during the drilling process and at the same time to improve the hole finish by controlling fraying and splintering. By controlling the main issues in the drilling process such as feed rate, cutting speed, thrust force, and torque generated in addition to the tool geometry, it is possible to optimize the drilling process avoiding the conventionally encountered problems.

Artificial Neural Networks Used As a Rule Selector for Fuzzy Logic Controllers

1993 ◽  
Author(s):  
Shou-Heng Huang ◽  
Ron M. Nelson
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Absolute Error ◽  
Operating Conditions ◽  
Delta Rule ◽  
Rule Sets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
And Control

Abstract A feedforward, three-layer, partially-connected artificial neural network (ANN) is proposed to be used as a rule selector for a rule-based fuzzy logic controller. This will allow the controller to adapt to various control modes and operating conditions for different plants. A principal advantage of an ANN over a look up table is that the ANN can make good estimates to fill in for missing data. The control modes, operating conditions, and control rule sets are encoded into binary numbers as the inputs and outputs for the ANN. The General Delta Rule is used in the backpropagation learning process to update the ANN weights. The proposed ANN has a simple topological structure and results in a simple analysis and relatively easy implementation. The average square error and the maximal absolute error are used to judge if the correct connections between neurons are set up. Computer simulations are used to demonstrate the effectiveness of this ANN as a rule selector.

Fuzzy Logic Control of a Laboratory Magnetic Levitation System

2003 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Piyush Jain ◽  
Devendra P. Garg
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Rate Of Change ◽  
Control Laboratory ◽  
Logic Control ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
And Control

This paper deals with the Fuzzy Logic control of a Magnetic Levitation system [1] available in the Robotics and Control Laboratory at Duke University. The laboratory Magnetic Levitation system primarily consists of a metallic ball, an electromagnet and an infrared optical sensor. The objective of the control experiment is to balance the metallic ball in a magnetic field at a desired position against gravity. The dynamics and control complexity of the system makes it an ideal control laboratory experiment. The student can design their own control schemes and/or change the parameters on the existing control modes supplied with the Magnetic Levitation system, and evaluate and compare their performances. In the process, they overcome challenges such as designing various control techniques, choose which specific control strategy to use, and learn how to optimize it. A Fuzzy Logic control scheme was designed and implemented to control the Magnetic Levitation system. Position and rate of change of position were the inputs to Fuzzy Logic Controller. Experiments were performed on the existing Magnetic Levitation system. Results from these experiments and digital simulation are presented in the paper.

Modeling and Control Techniques for Microstructure Development

2014 ◽  
Vol 541-542 ◽  
pp. 317-323
Author(s):  
R. Karthikeyan ◽  
R.K. Ganesh Ram ◽  
V. Kalaichelvi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
True Stress ◽  
Microstructure Development ◽  
Modeling And Control ◽  
Control Techniques ◽  
Hot Compression Test ◽  
Strain Data ◽  
Sic Composites ◽  
And Control

True stress-strain data is obtained for 6061Al/ 10% SiC composites by hot compression test. Mathematical models for % volume of recrystallization and diameter of the recrystallized grains are developed with process parameters such as strain, strain rate and temperature. These models are applied for optimization of the grain size and % volume of recrystallization. An attempt has been made to control microstructure evolution during hot deformation using fuzzy logic controller through simulation in MATLAB software. The fuzzy logic controller parameters are tuned using genetic algorithm.

scholarly journals Modelling and Controller Design for Non- Linear Two Tank Interacting System

2020 ◽  
Vol 9 (6) ◽  
pp. 82-85
Keyword(s):  
Fuzzy Logic ◽  
Comparative Study ◽  
Mathematical Modelling ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Linear Process ◽  
Matlab Simulink ◽  
Non Linear ◽  
And Control

This paper explains the mathematical modelling and controller design of Two Tank Interacting System (TTIS) for a non-linear process. To design the non-linear process using Matlab Simulink and control the process using conventional PID controller and Fuzzy Logic Controller (FLC). A comparative study was conducted extensively made to examine which controller suits well for the non-linear process through the response observed.

scholarly journals Optimization and control of water pumping PV systems using fuzzy logic controller

2019 ◽  
Vol 5 ◽  
pp. 853-865 ◽  
Author(s):  
Mustapha Errouha ◽  
Aziz Derouich ◽  
Saad Motahhir ◽  
Othmane Zamzoum ◽  
Najib El Ouanjli ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Pv Systems ◽  
Water Pumping ◽  
Optimization And Control ◽  
And Control

scholarly journals Hybrid Microgrid Energy Management and Control Based on Metaheuristic-Driven Vector-Decoupled Algorithm Considering Intermittent Renewable Sources and Electric Vehicles Charging Lot

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3423 ◽  
Author(s):  
Tawfiq M. Aljohani ◽  
Ahmed F. Ebrahim ◽  
Osama Mohammed
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Synchronous Generator ◽  
Photovoltaic System ◽  
Severe Condition ◽  
Electric Vehicle Charging ◽  
Bidirectional Converter ◽  
And Control

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels.

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