Fuzzy logic and genetic algorithm technique for non-liner system of overhead crane

Petrenko, Y. N.; Alavi, Seyed Mohammad

doi:10.1109/sibircon.2010.5555013

Cited by 10 publications

(8 citation statements)

References 2 publications

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“…Sunntech STb134.12/Tp represented the PV and its technical data is showing in table 2. Six PV modules connected in series formed the PV array which its total capacity is 810 W. The 105v DC input voltage was stepped down to 48v to fit the battery voltage level using a buck converter [11][12][13][14].…”

Section: Simulations and Results For Flc And Pando Mppt Techniquesmentioning

confidence: 99%

Adapting Model Predictive Control for a PV Station and Evaluating two different MPPT Algorithms P&O and FLC

Elzein¹,

Petrenko²

2017

Adv. sci. technol. eng. syst. j.

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Section: Simulations and Results For Flc And Pando Mppt Techniquesmentioning

confidence: 99%

Adapting Model Predictive Control for a PV Station and Evaluating two different MPPT Algorithms P&O and FLC

Elzein¹,

Petrenko²

2017

Adv. sci. technol. eng. syst. j.

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Section: Simulations and Results For Flc And Pando Mppt Techniquesmentioning

confidence: 99%

A Structure Approach for a Photovoltaic Station Control Based on Adaptive Fuzzy Agent

Elzein¹,

Petrenko²

2017

Sist. anal.i prikl. inform.

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The solar energy is directly converted into electrical energy by solar PV module. Each type of PV module has its own specific characteristic corresponding to the surrounding condition such as irradiation, and temperature and this makes the tracking of maximum power point (MPP) a complicated problem. To overcome this problem, many maximum power point tracking (MPPT) control algorithms have been presented. Fuzzy logic (FL) has been used for tracking the MPP of PV modules because it has the advantages of being robust, relatively simple to design and does not require the knowledge of an exact model where a mathematical model of the PV module, DC-DC converter, are used in the study of FL based MPPT algorithm. It is suggested to present this problem in the form of two-folds; first to identify the deviation of the power to maximum power point, and secondly, to control the voltage of the DC-DC converter corresponding to maximum power. In this paper, the first discussion approach will stress out the integration of model predictive control in maximum power point tracking MPPT and as progressing a second approach is identified as fuzzy logic controller FLC and perturb & Observe P&O algorithms are analyzed. All are interrelated to MPPT model for a photovoltaic module, PVM, to search for and generate the maximum power; in this case what’s called P-max. As per the first technique the focus is on the optimal duty ratio, D, for a series of multi diverse types of converters and load matching. The design of the MPPT for a stand-alone photovoltaic power generation system is applied where the system will consist of a solar array with nonlinear time varying characteristics, and a converter with appropriate filters. The integration of model predictive control will be addressed first in this paper. The second fold will implement an MPPT system that use the FLC and compare it with a classical MPPT P&O algorithm through the utilization of Simulink. The novel design in the FLC will be based on the use of asymmetrical membership functions to compensate for the asymmetrical P-V curve of solar panel.

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“…To conduct such navigations in unknown environments at the presence of mobile or immobile obstacles, without any knowledge of the coordinates and movement function of the obstacles, we can use the genetic algorithm. Due to the fact that the length of the pathway is variable, we can also use the variable chromosome structure (since the different pathways are of different length) [11]. In other words, each gene would determine the number of a mid-node along the way (i.e.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Routing Therapist Nano Robots in a Dynamic Environment of the Human Body with the Help of Fuzzy Logic

Naderloo¹,

Naderloo²

2016

Iran J Med Inform

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Introduction: Using nanotechnology, scientists have been able Nano robot knowledge that the human body is designed to be therapeutic and protective role and play therapist. This Nano robot could help in different ways such as a capsule or injection into the vascular system of the body, the human body and controlled or programmed manner (smart) to play the role of a therapist. . These robots can perform a routing optimization in the shortest time and shortest distance traveled, upon detection of possible damage, damage to the site and using nanotechnology to repair it.Material and Methods: In this class of problems to find the path or paths (preferably optimum), which move the robot without colliding with obstacles static/dynamic of a specific origin to its destination. In this paper presents a simulation system under manual control, wireless routing protocol, and is used for navigation Nano robot because of the problems raised in writing and to fix bugs of the system, an intelligent routing system, To move Nano robot therapist, based on the fuzzy control system in which the steering motor in the vicinity of obstacles and destination point for platform Nano robot be issued.Results: For the more accurate and more complete routing in dynamic environments without the structure of the human body (the restrictions are not predetermined and fixed), need to work on issues relating to robot motion planning and to determine the optimal path will be moved. This research finally revealed that a system following the fuzzy logic for a mobile Nano robot navigation.Conclusion: The present findings from the implemented designs here took into account the merits and demerits and evaluated the optimal navigation issue in robots following the fuzzy logic and the genetic algorithm. We found out that to solve such issues more efficiently.

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Fuzzy logic and genetic algorithm technique for non-liner system of overhead crane

Cited by 10 publications

References 2 publications

Adapting Model Predictive Control for a PV Station and Evaluating two different MPPT Algorithms P&O and FLC

Adapting Model Predictive Control for a PV Station and Evaluating two different MPPT Algorithms P&O and FLC

A Structure Approach for a Photovoltaic Station Control Based on Adaptive Fuzzy Agent

Intelligent Routing Therapist Nano Robots in a Dynamic Environment of the Human Body with the Help of Fuzzy Logic

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