New Lyapunov function for Takagi-Sugeno discrete time uncertain systems

Bouyahya, Ali; Manai, Yassine; Haggège, Joseph

doi:10.1109/icmic.2015.7409347

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Probably the most known model to implement an FIS is to use the Takagi–Sugeno approach [ 29 , 30 , 31 , 32 , 33 ]. Similar to other fuzzy methods, this model description uses the fuzzy specific IF-THEN rules.…”

Section: Fuzzy Calibration Of Microscopic Traffic Modelsmentioning

confidence: 99%

“…A general Takagi–Sugeno FIS for continuous-time models can be described by the following fuzzy rules [ 29 , 30 ]:

where the notations have the following meaning:

are the premise variables;

represent the fuzzy sets;

is the number of defined fuzzy rules;

is the state vector;

is the input vector;

is the state matrix;

is the input matrix;

is the output matrix where q is the number of output parameters;

are the matrices that incorporate the uncertainties. …”

Section: Fuzzy Calibration Of Microscopic Traffic Modelsmentioning

confidence: 99%

“…A general Takagi-Sugeno FIS for continuous-time models can be described by the following fuzzy rules [29,30]:…”

Section: Takagi-sugeno Fismentioning

confidence: 99%

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Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration

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David

et al. 2020

Sensors

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Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.

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