Modelling, simulation and performance comparison of different membership functions based fuzzy logic control for an active magnetic bearing system

Gupta, Suraj; Biswas⃰, Pabitra Kumar; Aljafari, Belqasem; Babu, Thanikanti Sudhakar; Das, Sajal K.

doi:10.1049/tje2.12229

Cited by 3 publications

(4 citation statements)

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“…Similar comparative studies were conducted in various fields, such as optical [19] and wireless [20] networks, magnetic bearing system control [21], chaotic time series prediction [22], evaluation of user experience for specific applications [23], and streamflow prediction in the hydrological field [24]. Although Takagi-Sugeno usually showed better results, the research in the hydrological domain showed that the Mamdani FIS is more suitable.…”

Section: Literature Reviewmentioning

confidence: 70%

“…Fuzzification is responsible for converting the numerical input values (i.e., the observed relative velocity RELATIVE_VELOCITY and the spacing I NTERVEH ICLE_SPACI NG between the LV and FV at time t − τ) into linguistic variables. The names chosen for the linguistic variables are similar to those existing in other similar studies [21,27,39,40], such as negative big (NB), negative small (NS), zero (ZE), positive small (PS), and positive big (PB). The knowledge base contains the rules expressed as "IF THEN" conditions that define the logical relations between the inputs RELATIVE_VELOCITY and I NTERVEH ICLE_SPACI NG and the output s_OFFSET_x, where x = {M; TS} is defined separately for Mamdani (Section 3.2.2) and Takagi-Sugeno FISs (Section 3.2.3).…”

Section: Fuzzy-based Calibration Of Continuous-time Car-following Modelsmentioning

confidence: 99%

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Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Pop,

Pescaru,

Micea

2023

Sensors

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The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi–Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi–Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi–Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.

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Section: Literature Reviewmentioning

confidence: 70%

Section: Fuzzy-based Calibration Of Continuous-time Car-following Modelsmentioning

confidence: 99%

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Pop,

Pescaru,

Micea

2023

Sensors

View full text Add to dashboard Cite

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“…A membership function is a function that can work in mapping points in the input data to their membership values. Membership functions that are generally used and have advantages in representing and have broader capabilities in forming various forms of distribution are Gaussian and Generalized Bell membership functions (Gupta et al, 2023).…”

Section: Data Pre-processingmentioning

confidence: 99%

Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters

Fauzi,

Erlinda,

Arum

2024

JTAM

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Hydrometeorological disasters are one of the disasters that often occur in big cities like Semarang. Hydrometeorological disasters that often occur are floods caused by high-intensity rainfall in the area. Early mitigation needs to be done by knowing about future rain. Rainfall data in Semarang City fluctuates, so the Adaptive Neuro-Fuzzy Inference System (ANFIS) method approach is very appropriate. This research will use the Grid Partitioning (GP) approach to produce more accurate forecasting. The data used in this research is daily rainfall observation data from the Meteorology Climatology Geophysics Agency (BMKG). The membership functions used are Gaussian and Generalized Bell. The two membership functions will be compared based on the RMSE and MAPE values to get the best one. The data used in this research is daily rainfall data. Rainfall in Semarang City every month experiences anomalies, which can result in flood disasters. The ANFIS-GP method with a Gaussian membership function is the best, with an RMSE value of 0.0898 and a MAPE of 5.2911. Based on the forecast results for the next thirty days, a rainfall anomaly of 102.53 mm on the thirtieth day could cause a flood disaster.

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“…The mapping between 0 and 1 for each data point in the input space is determined by a membership function in the form of a fuzzy curve [85]. Membership in fuzzy sets has different shapes consisting of linear, bell, gaussian, trapezoidal, shoulder curves (left and right), and triangular shapes [103], [104]. In this study, only two types of curves were used, namely the shoulder curve (left and right) and the triangle curve.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

Furizal,

Sunardi,

Yudhana

2023

Journal of Robotics and Control

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Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.

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Modelling, simulation and performance comparison of different membership functions based fuzzy logic control for an active magnetic bearing system

Cited by 3 publications

References 45 publications

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Mamdani vs. Takagi–Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models

Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters

Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things

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