Self-Tuning Fuzzy Control of a Rotary Dryer

Pirrello, L.; Yliniemi, Leena; Leiviskä, Kauko; Galluzzo, M.

doi:10.3182/20020721-6-es-1901.01172

Cited by 9 publications

(5 citation statements)

References 3 publications

(3 reference statements)

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“…Berbagai metode pengendali telah diterapkan pada mesin pengering untuk memperbaiki kinerjanya. Pada penelitian [12], dirancang self-tuning fuzzy controller pada rotary dryer yang banyak ditemukan pada pengering di industri dengan parameter temperatur dan kelembaban yang menjadi hal penting untuk dikendalikan agar pengering memiliki kinerja yang baik. Penelitian [13] menerapkan metode fuzzy logic dan computer vision untuk mengendalikan sebuah hybrid hot air-infrared drying process yang digunakan untuk mengeringkan buah.…”

Section: Pendahuluanunclassified

Perancangan Prototipe Sistem Kendali Otomatis Pada Pengering Pakaian Berbasis Air Heater

Priyandha

Wati²

2023

JJEEE

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Pengering pakaian diharapkan dapat bekerja secara otomatis dan dapat menjaga kualitas pakaian yang dikeringkan. Terdapat parameter penting pada proses pengeringan pakaian yaitu temperatur dan kelembaban. Untuk menjaga kualitas pakaian, perlu diperhatikan nilai temperatur pengeringan agar tidak melebihi batas temperatur pengeringan yang ditentukan pada setiap jenis pakaian. Oleh karena itu, diperlukan pengaturan setpoint temperatur pengeringan pakaian pada alat pengering pakaian. Pada penelitian ini dirancang prototipe pengendali yang dapat bekerja secara otomatis pada sebuah sistem pengering berbasis air heater. Prototipe yang dirancang terdiri atas air heater, sensor temperatur, sensor kelembaban, Arduino, dan tampilan antarmuka menggunakan LabVIEW. Proses pengeringan dioperasikan oleh pengguna melalui antarmuka dimana pengguna memasukan dua input yaitu jenis pakaian yang akan dikeringkan dan jumlahnya. Input tersebut digunakan pada penentuan nilai temperatur operasi pada air heater. Selama proses pengeringan, nilai temperatur maupun nilai kelembaban pada bilik pengering diukur menggunakan sensor tipe DHT22. Kedua nilai lalu tersebut ditampilkan melalui antarmuka LabVIEW. Nilai temperatur tersebut dibandingkan dengan nilai temperatur setpoint yang ditentukan berdasarkan jenis dan jumlah pakaian yang akan dikeringkan. Berdasarkan nilai perbandingan tersebut, LabVIEW mengirimkan nilai kendali berupa nilai PWM secara serial ke Arduino, yang merupakan sinyal kendali ke air heater. Kemudian, air heater bekerja menjaga temperatur pada bilik pengeringan agar sesuai setpoint selama proses pengeringan berjalan atau kondisi timer on. Jika nilai kelembaban telah mencapai kriteria kondisi kering dan waktu pengeringan telah habis, maka air heater akan masuk pada mode off. Berdasarkan hasil pengujian, prototipe sistem kendali yang dirancang mampu bekerja secara otomatis dan hasil pengeringan dapat mempertahankan kualitas pakaian. Selain itu penghematan energi listrik juga dimungkinkan dengan penggunaan pengendali otomatis pada pengering pakaian tersebut.The clothes dryer is expected to work automatically and can maintain the quality of the clothes. Temperature and humidity are the two main parameters in the process of drying clothes. The drying temperature affects the clothes conditions because each type of clothing has a certain temperature limit to maintain its quality. Therefore, it is necessary to set the temperature setpoint for drying clothes operation. This study designed a controller prototype that can work automatically in an air heater-based clothes dryer. The designed prototype consists of an air heater, temperature sensor, humidity sensor, Arduino, and an interface using LabVIEW. The drying process is operated by the user through an interface where the user inputs the type of clothing and the amount of clothing to be dried. The input is used to determine the temperature setpoint on the air heater. During the drying process, the temperature and humidity values in the drying chamber are read by the DHT22 sensor and then displayed on the interface. This temperature value is compared with the setpoint temperature which is determined based on the type and amount of clothes to be dried. Based on the comparison value, LabVIEW sends control values in the form of PWM values serially to Arduino, which are control signals to the air heater. Then, the air heater works to maintain the temperature in the drying chamber to match the setpoint during the drying process or when the timer is on. The air heater will turn off when the humidity has reached dry conditions and the drying time has been over. Based on the testing results, the control system prototype is able to work automatically, and the drying process can maintain the quality of the clothes. In addition, saving electrical energy is also possible with the use of the automatic controller.

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Section: Pendahuluanunclassified

Perancangan Prototipe Sistem Kendali Otomatis Pada Pengering Pakaian Berbasis Air Heater

Priyandha

Wati²

2023

JJEEE

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“…Several mathematical functions are used to find the logic between these inputs and outputs. The binary (classical) system is a special case of fuzzy logic [48]. The fuzzy controller shown in Figure 2 consists of the following four components: A set of rules (in the form of If‐Then) contains the knowledge base provided by the expert on how best to control the system. The fuzzification interface modifies the inputs.…”

Section: System Modelizationmentioning

confidence: 99%

“…They can be interpreted and compared to the base rules. The inference mechanism evaluates the inputs at the current time. Based on the check, the rules decide which output is relevant. The defuzzification interface converts the output values into decisions [48]. …”

Section: System Modelizationmentioning

confidence: 99%

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Queries allocation in WSNs with fuzzy control system

Abbassi

Ezzedine

2023

IET Communications

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Processing queries in wireless sensor networks causes quality of service challenges such as data latency and validity. In fact, this paper is going to shed light on identifying the right trade‐off between latency and the freshness of the data. For this purpose, a fuzzy logic‐based decision‐making system that assigns incoming queries either to the sensor network, where the queries are waiting to be processed into a queue, or a central database which provides stored and potentially stale data or to a cache server which in turn provides acceptable latency and recent data, is provided. Thus, a new rule‐based system decision is introduced, that minimizes the request latency and returns the most recent data. In addition, a comparison between the approach and other work that is restricted to the database server and sensor network with no focus on the caching server is provided. This paper is going to carry on with the theory of Markov Chain which is more complicated compared to the fuzzy logic system. This approach is confirmed in the basis of an experience is developed with python.

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“…Adaptation, which is essential in varying operating conditions, is an important part of fuzzy systems: scaling, modifying membership functions and updating rules are supported in many ways. There are many mechanisms for self-organising [6] and self-tuning [7] fuzzy controllers. Powering modifiers can be used for the fuzzy numbers and labels [8].…”

Section: Introductionmentioning

confidence: 99%

Expertise and Uncertainty Processing with Nonlinear Scaling and Fuzzy Systems for Automation

Juuso

2020

Open Engineering

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Integration of domain expertise and uncertainty processing is increasingly important in automation solutions which rely on data analytics and artificial intelligence. We need a level to assess what is approximately correct. Uncertainties of the inputs are taken into account by using fuzzy numbers as the inputs of different fuzzy and parametric systems. Nonlinear scaling functions (NSFs) integrate these solutions and make them easier to tune. Fuzzy rule-based systems are represented with scaled fuzzy inputs. Membership functions (MFs) can be developed from NSFs and existing MFs can be used in developing NSFs. Fuzzy set systems and linguistic equation (LE) systems become consistent within the limits of detail. In recursive analysis, both meanings and interactions on all levels can be tuned together with genetic algorithms. In applications, the modular overall system consists of similar subsystems, which are normally used, with extensions to fuzzy. The compact fuzzy modules can be developed for specific tasks which are combined within Cyber Physical Systems (CPS). Uncertainty processing is embedded in the recursive analysis. The fuzzy extensions provide a feasible way for the sensitivity analysis of the solution.

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Self-Tuning Fuzzy Control of a Rotary Dryer

Cited by 9 publications

References 3 publications

Perancangan Prototipe Sistem Kendali Otomatis Pada Pengering Pakaian Berbasis Air Heater

Perancangan Prototipe Sistem Kendali Otomatis Pada Pengering Pakaian Berbasis Air Heater

Queries allocation in WSNs with fuzzy control system

Expertise and Uncertainty Processing with Nonlinear Scaling and Fuzzy Systems for Automation

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