Development of Fuzzy Logic Control  for Vehicle Air Conditioning System

Nasution, Henry

doi:10.12928/telkomnika.v6i2.553

Cited by 22 publications

(14 citation statements)

References 14 publications

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“…In CFGA [26] BS creates a balanced cluster based on Genetic-fuzzy based algorithm. As the BS situated in central of the network and all nodes check their validity for CH in fuzzy module consumes more energy of nodes and lifetime of network decreases and they does not show how multi-hop routing works [27], [28]. In [29] A-star based algorithm (ASSER), BS find optimal route and broadcast in the twotier network, by which CH send data to BS using optimal route, which leads to balance the traffic load.…”

Section: Related Workmentioning

confidence: 99%

Genetic-fuzzy based load balanced protocol for WSNs

Kashyap¹,

Kumar²

2019

IJECE

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<p><span>Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head.</span></p>

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Section: Related Workmentioning

confidence: 99%

Genetic-fuzzy based load balanced protocol for WSNs

Kashyap¹,

Kumar²

2019

IJECE

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“…Berbagai penelitian sebelumnya menunjukkan bahwa sistem kecerdasan buatan dapat diwujudkan dengan berbagai macam algoritma, yang salah satunya adalah logika Fuzi. Logika Fuzi ini telah diterapkan di berbagai bidang, demikian pula di bidang robotika [1][2][3][4][5].…”

Section: Pendahuluanunclassified

Penerapan Multi-Mikrokontroler Pada Model Robot Mobil Berbasis Logika Fuzi

Widodo

2009

TELKOMNIKA

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PENDAHULUANRobot dalam segala bentuk dan fungsinya adalah salah satu hasil kemajuan teknologi yang banyak membantu aktivitas manusia. Penggunaan tersebar dari dunia militer, medis, sampai rumah tangga. Di dunia militer dan penanggulangan bencana, fenomena ini umumnya didasari pada keinginan yang kuat untuk mengurangi jumlah korban jiwa manusia pada berbagai tugas militer. Pada kebutuhan industri dan rumah tangga pada umumnya penggunaan robot didorong oleh keinginan untuk menjadikan robot sebagai pengganti pekerja manusia pada hal-hal yang sesuai dan menuntut untuk hal tersebut. Robot mobil adalah bentuk robot yang paling banyak digunakan pada kebutuhan rumah tangga dan bahkan pada industri. Untuk dapat menjalankan tugasnya maka robot mobil yang dirancang haruslah mampu melakukan hal-hal berikut: 1. Melakukan pergerakan secara autonomous/tanpa dikendalikan operator dengan sistem navigasi yang dimilikinya. 2. Mencari atau mendeteksi obyek atau benda yang menjadi bagian dari tugasnya. 3. Melakukan tindakan terhadap obyek sesuai dengan fungsi dari robot tersebut.Navigasi sebagai salah satu kemampuan dasar untuk robot tersebut telah menjadi topik yang sering diangkat dalam penelitian robotika [1][2]. Walaupun ada kemungkinan untuk merancang sistem navigasi robot autonomous tanpa sistem kecerdasan, namun robot jenis ini mensyaratkan adanya kondisi tertentu pada lingkungannya agar dapat beroperasi, misalnya robot yang mengikuti garis. Jadi sistem kecerdasan buatan adalah suatu hal yang mutlak ada dalam robot yang akan digunakan pada suatu daerah kerja yang belum direkayasa untuk

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“…The controller by constructing a Takagi and Sugeno-type fuzzy rulebased system then employing BP learning algorithm combined with the Least Squares method to optimize the Membership Function (MF) parameters (Wu Jian and Cai Wenjian, 2000). Nasution (2008 and2011) presented fuzzy logic algorithm to evaluate the saving energy by continuously regulates the compressor speed. Researcher have addressed the air conditioning system for special cases such as buildings furnished which perform indoor air quality control by various classical control methods or intelligent approaches to keep the inside environment at comfortable temperature and humidity levels (Arguelle and Velez, 1999), (Rafeal and Jorge, 2003) and ( Huaguang and Chai, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…However, all of these approaches do not pay any attention to one important parameter of the heated, ventilated and conditioned air which is the pressure. There are wide range of applications that involve the HVAC and refrigeration of gases that are not maintained at atmospheric pressure (Saleh and Al-Nimr, 2006, 2007, 2008. Examples of these systems are conditioning systems that operate at high altitudes or below sea level or systems that have to cool certain products maintained at high pressures as in many industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Developed adaptive neuro-fuzzy algorithm to control air conditioning system at different pressures

Al-Jarrah¹,

Al‐Jarrah²

2018

Int. J. Eng. Sci. Tech

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The paper developed artificial intelligence technique adaptive neuro-fuzzy controller for air conditioning systems at different pressures. The first order Sugeno fuzzy inference system was implemented and utilized for modeling and controller design. In addition, the estimation of the heat transfer rate and water mass flow rate in/out the system was determined by fuzzy IF-THEN rules. The combination between back propagation algorithm and least square method helps to optimize the membership functions and generated the fuzzy rules that describe the relationship between the input/output data of the air conditioning system which changes with pressure values. The fuzzy rules are tuned by adaptive neural fuzzy inference system ANFIS. Statistical indices such as Root Mean Square Error (RMSE) and mean relative error (MRE) are used to evaluate performance of the ANFIS model. The values of the indices show that ANFIS model can accurately and reliably be used to air conditioning system and the simulated model of the system shows a good performance results.

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Development of Fuzzy Logic Control for Vehicle Air Conditioning System

Abstract: Abstract

Cited by 22 publications

References 14 publications

Genetic-fuzzy based load balanced protocol for WSNs

Genetic-fuzzy based load balanced protocol for WSNs

Penerapan Multi-Mikrokontroler Pada Model Robot Mobil Berbasis Logika Fuzi

Developed adaptive neuro-fuzzy algorithm to control air conditioning system at different pressures

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