scholarly journals Fuzzy-Based Temperature Controller for Culturing Mesophilic and Thermophilic Bacterial using Firing Angle

2021 ◽  
Vol 11 (04) ◽  
pp. 1-11
Author(s):  
Iliya Tizhe Thuku ◽  
Allo Iliya Alhassan ◽  
Adamu Shuaibu Kadalla
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Process Development ◽  
Thermophilic Bacteria ◽  
Research Work ◽  
Medical Laboratory ◽  
Optimum Temperature ◽  
Medical Diagnoses ◽  
Laboratory Culturing ◽  
And Control

Temperature control is an important parameter in the fields of engineering and medical Laboratory. Culturing of micro bacteria and enzymes for the purpose of medical diagnoses or process development in manufacturing industries require their optimum temperature to be monitored and control. In this research work Fuzzy logic controller was designed to control the incubator Temperature, by computing appropriate firing angle. MATLAB Simulink toolbox was used for simulation. The results show that the fuzzy logic controller tracks the optimum Temperature for culturing Mesophilic and Thermophilic bacteria at 37.5oC and 55.2oC respectively. The transient response shows an overshot of 0.5% for the two responses. The rise time were 787ms and 792ms for 310.5 Kelvin and 328.2 Kelvin respectively. The settling time for the 310.5 Kelvin response was 1s; whereas it took 2s for the 328.2 Kelvin response to attain steady state.

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.

Implementation of the Type-2 Fuzzy Controller in PLC

2010 ◽  
Vol 164 ◽  
pp. 95-98 ◽  
Author(s):  
Ireneusz Dominik
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Research Work ◽  
Problem Type ◽  
Object A ◽  
Develop Type ◽  
Linear Object

The main aim of the presented research work was to develop type-2 fuzzy logic controller, which by its own design should be “more intelligent” than type-1. Along with the intelligence it should provide better results in solving a particular problem. Type-2 fuzzy logic controller is not well-known and it is rarely used at present. The idea of type-2 fuzzy logic set was presented by Zadeh in 1975, shortly after the presentation of type-1 fuzzy set. At the beginning scientists and researchers worked on type-1. Only after developing type-1 the attention was directed towards the type-2. The first applications of type-2 fuzzy logic in control appeared in 2003. The fuzzy logic controller type-2 was tested experimentally by controlling a non-linear object: a shape memory alloy (SMA) actuator DM-01PL, made by Miga Motor company, which despite small size distinguishes itself by its 9 N strength. Comparison of experimental data of the fuzzy logic controller type-2 and type-1 clearly indicates the superiority of the former, particularly in reducing signal overshoots.

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 THE MONITORING AND CONTROL OF AN ARM CRANK SYSTEM USING FUZZY LOGIC FOR MULTI-HANDICAPPED YOUTHS WITH MENTAL RETARDATION

2002 ◽  
Vol 14 (05) ◽  
pp. 197-203
Author(s):  
JIANN-SHING SHIEH ◽  
JEN-TSANG HUNG
Keyword(s):  
Fuzzy Logic ◽  
Mental Retardation ◽  
Fuzzy Logic Controller ◽  
Physical Therapist ◽  
Rotation Velocity ◽  
Manual Control ◽  
Control Group ◽  
Monitoring And Control ◽  
Significant Difference ◽  
And Control

In this paper, we try to cope with National Special School in Taoyuan in order to fit the special requirements for multi-handicapped youths with mental retardation using current high technology, such as microcomputer control, sensing technology, and fuzzy logic control. The prototype device of this paper is focused on designing an arm crank system using a fuzzy logic controller (FLC) to exercise for the upper limb. The rotation velocity of this system was controlled via adjusting the electric current of the brake in order to increase the training motivation. Hence, a FLC was designed into this system in comparison with a manual control by an expert (i.e., physical therapist). Fifteen multi-handicapped youth with mental retardation were treated using the manual control as a control group, and another fifteen youths were treated using FLC as an experimental group. The root mean square deviation (RMSD) of the change of rotation velocity of arm crank was no significant difference (p > 0.05) using Student t test analysis. It means that FLC can be replaced the manual control by the qualified physical therapist. Therefore, it can be seen as a demonstration of feasibility of the applicability of this FLC for monitoring and control the velocity of the arm crank. But, it still needs a longer series of multi-handicapped youths with mental retardation, perhaps to refine the rule-bases, and certainly to see how widely they are applicable.

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