scholarly journals A revisit to the past plague epidemic (India) versus the present COVID-19 pandemic: fractional-order chaotic models and fuzzy logic control

2021 ◽  
Author(s):  
Manashita Borah ◽  
Binoy Krishna Roy ◽  
Tomasz Kapitaniak ◽  
Karthikeyan Rajagopal ◽  
Christos Volos
Keyword(s):  
Fuzzy Logic ◽  
Fractional Order ◽  
Fuzzy Logic Control ◽  
The Past ◽  
Logic Control

scholarly journals A NOVEL ENHANCED MPPT METHOD COMBINING FRACTIONAL-ORDER AND FUZZY LOGIC CONTROL

2020 ◽  
Vol 4 (5) ◽  
pp. 22-28
Author(s):  
VIJAYA KUMAR S ◽  
MADHU SUDHAN REDDY M
Keyword(s):  
Fuzzy Logic ◽  
Fractional Order ◽  
Fuzzy Logic Control ◽  
Maximum Power ◽  
Tracking Accuracy ◽  
Pv System ◽  
Maximum Power Point ◽  
Logic Control ◽  
Alpha Factor ◽  
Power Point

A fractional-order fuzzy logic control (FOFLC) method for maximum power point tracking (MPPT) in a photovoltaic (PV) system is presented. By combining the robustness of fuzzy logic with the accuracy of fractional order, the proposed method can improve the tracking accuracy in weather variations compared with the conventional fuzzy MPPT. First, the fractionalorder factor is carefully selected according to the dynamic range of the fuzzy controller. It takes a bigger alpha factor in the first place to expand the fuzzy domain and shortens the time of searching for the MPP. When the maximum power point is approached, it uses a smaller the alpha factor to contract the fuzzy domain and eliminates the oscillations at the MPP. Therefore, the FOFLC in a PV system has rapid dynamic responses under environment variations and high tracking accuracy of the maximum power point. Second, MATLAB/Simulink software is employed to simulate a PV power system and verify the proposed algorithm by various simulations. The enhanced MPPT algorithm has been implemented on a field programmable gate array (FPGA) board. Finally, a boost dc–dc converter experiment has been carried out to evaluate the system performance. The simulation and experiment results show that this method can improve the transient and steady-state performance simultaneously.

An Enhanced MPPT Method Combining Fractional-Order and Fuzzy Logic Control

2017 ◽  
Vol 7 (2) ◽  
pp. 640-650 ◽  
Author(s):  
Shiqing Tang ◽  
Yize Sun ◽  
Yujie Chen ◽  
Yiman Zhao ◽  
Yunhu Yang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Fractional Order ◽  
Fuzzy Logic Control ◽  
Logic Control

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

A contribution to the identification in fuzzy-logic control

1990 ◽  
Vol 55 (4) ◽  
pp. 951-963 ◽  
Author(s):  
Josef Vrba ◽  
Ywetta Purová
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Automatic Generation ◽  
Linguistic Variable ◽  
Fuzzy Subset ◽  
Input Output ◽  
Logic Control ◽  
Correlation Tables

A linguistic identification of a system controlled by a fuzzy-logic controller is presented. The information about the behaviour of the system, concentrated in time-series, is analyzed from the point of its description by linguistic variable and fuzzy subset as its quantifier. The partial input/output relation and its strength is expressed by a sort of correlation tables and coefficients. The principles of automatic generation of model statements are presented as well.

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