Fuzzy observer for state estimation of the METANET traffic model

Traffic control has proven an effective measure to reduce traffic congestion on freeways. In order to determine appropriate control actions, it is necessary to have information on the current state of the traffic. However, not all traffic states can be measured (such as the traffic density) and so s...

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Bibliographic Details
Main Authors: Hidayat, Z., Lendek, Zs, Babuska, R., De Schutter, B.
Format: Conference Proceeding
Language:English
Subjects:
Approximation methods
Computational modeling
Equations
Mathematical model
Nonlinear systems
Observers
Traffic control
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Summary:Traffic control has proven an effective measure to reduce traffic congestion on freeways. In order to determine appropriate control actions, it is necessary to have information on the current state of the traffic. However, not all traffic states can be measured (such as the traffic density) and so state estimation must be applied in order to obtain state information from the available measurements. Linear state estimation methods are not directly applicable, as traffic models are in general nonlinear. In this paper we propose a nonlinear approach to state estimation that is based on a Takagi-Sugeno (TS) fuzzy model representation of the METANET traffic model. By representing the METANET traffic model as a TS fuzzy system, a structured observer design procedure can be applied, whereby the convergence of the observer is guaranteed. Simulation results are presented to illustrate the quality of the estimate.
ISSN:2153-0009
2153-0017
DOI:10.1109/ITSC.2010.5625223