Comparison of Mamdani and Sugeno Fuzzy Logic Performance as Speed Controller

Saleh, I.; Alhady, S. S. N.; Rahiman, Wan

doi:10.1007/978-981-10-1721-6_40

Cited by 3 publications

(3 citation statements)

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“…The existing literature also offers some comparisons between Mamdani and Takagi-Sugeno FISs related to intelligent transportation systems (ITSs), except for the topic of car-following calibration. Saleh et al [30] performed a comparison of these techniques in the design of a speed controller for a remote car, the analysis of the results showing better results in the case of the Takagi-Sugeno FIS. Another topic investigated is the prediction of traffic flow based on historical data [31], where the Takagi-Sugeno technique achieved faster processing times compared to Mamdani.…”

Section: Literature Reviewmentioning

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: 99%

“…The defuzzification of the Takagi-Sugeno FIS uses the weighted average technique for the output and considers the constant values defined for the five membership functions represented in Figure 7. Consequently, Equation ( 6) expresses the calculation of the crisp output Z as a weighted average of the total clipped singletons [30,31].…”

Section: Takagi-sugenomentioning

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

show abstract

“…The Mamdani engine combines fuzzy rules in mapping from fuzzy input group to fuzzy output group, while the Takagi-Sugeno type links blurred inputs to clear outputs. Eliminator converts a fuzzy set to a crisp number using the centroid of the region, the median of the region, the mean of the maximum, or the extreme criteria [25].…”

Section: Neuro-fuzzy Techniquementioning

confidence: 99%