Comparative Study of a Super-Twisting Sliding Mode Observer and an Extended Kalman Filter for a Freeway Traffic System

Majid, Hirsh; Abouaïssa, Hassane

doi:10.1515/cait-2015-0034

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…The sliding mode control algorithm consists of two terms: a discontinuous term which ensures the stability of the system and an equivalent term which brings back the state of the system on the sliding surface [18], [19]:…”

Section: Condition Of Convergencementioning

confidence: 99%

Synthesis of SMC algorithms applied to wind generator

Abdelli¹,

Mezouar

Bendjebbar³

et al. 2021

IJPEDS

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<p class="Default">The use of the classical (SMC) applied to control of stator’s powers of DFIG, gives the problem of chattering, therefore to avoid this phenomenon a robust algorithm (STSMC) is applied. This paper presents a comparison of conventional SMC with the proposed strategy of STSMC algorithm. The results are obtained using MATLAB and demonstrate stability and robustness of this algorithm.</p>

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Section: Condition Of Convergencementioning

confidence: 99%

Synthesis of SMC algorithms applied to wind generator

Abdelli¹,

Mezouar

Bendjebbar³

et al. 2021

IJPEDS

View full text Add to dashboard Cite

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Traffic state estimation on highway: A comprehensive survey

Seo

Bayen

Kusakabe

et al. 2017

Annual Reviews in Control

326

174

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Traffic state estimation (TSE) refers to the process of the inference of traffic state variables (i.e., flow, density, speed and other equivalent variables) on road segments using partially observed traffic data. It is a key component of traffic control and operations, because traffic variables are measured not everywhere due to technological and financial limitations, and their measurement is noisy. Therefore, numerous studies have proposed TSE methods relying on various approaches, traffic flow models, and input data. In this review article, we conduct a survey of highway TSE methods, a topic which has gained great attention in the recent decades. We characterize existing TSE methods based on three fundamental elements: estimation approach, traffic flow model, and input data. Estimation approach encompasses methods that estimate the traffic state, based on partial observation and a priori knowledge (assumptions) on traffic dynamics. Estimation approaches can be roughly classified into three according to their dependency on a priori knowledge and empirical data: model-driven, data-driven, and streaming-data-driven. A traffic flow model usually means a physics-based mathematical model representing traffic dynamics, with various solution methods. Input data can be characterized by using three different properties: collection method (stationary or mobile), data representation (disaggregated or aggregated), and temporal condition (real-time or historical). Based on our proposed characterization, we present the current state of TSE research and proposed future research directions. Some of the findings of this article are summarized as follows. We present model-driven approaches commonly used. We summarize the recent usage of detailed disaggregated mobile data for the purpose of TSE. The use of these models and data will raise a challenging problem due to the fact that conventional macroscopic models are not always consistent with detailed disaggregated data. Therefore, we show two possibilities in order to solve this problem: improvement of theoretical models, and the use of data-driven or streaming-data-driven approaches, which recent studies have begun to consider. Another open problem is explicit consideration of traffic demand and route-choice in a large-scale network; for this problem, emerging data sources and machine learning would be useful.

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