The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Nacim Meslem - One of the best experts on this subject based on the ideXlab platform.
-
Robust set-membership State Estimator against outliers in data
IET Control Theory and Applications, 2020Co-Authors: Nacim Meslem, Ahmad HablyAbstract:Based on interval computation, a set‐membership State Estimator capable to manage a certain type of outliers in measurements is proposed for uncertain discrete‐time linear systems. To achieve this purpose, two set‐valued filtering techniques are implemented in the presented State estimation algorithm. The setting up of these techniques offers two main advantages. On one hand, the convergence of the estimated State enclosures width is guaranteed and, on the other hand, the algorithm robustness against outliers in data is ensured. That is, unlike former methods, the proposed set‐valued State Estimator preserves the framing property despite the presence of some false values in available sensors data. To show the efficiency and the performance of the introduced set‐valued State Estimator, it is compared, through two numerical examples, with an optimal interval observer selected from the literature for its high performance.
-
Forward-Backward Set-Membership State Estimator Based on Interval Analysis
2018Co-Authors: Nacim Meslem, Nacim RamdaniAbstract:Interval computation is used to design a set- membership State Estimator for uncertain discrete-time linear systems. Based on the observability property, an output set-version technique is applied to discard all the parts of the predicted State enclosure that are inconsistent with systemoutput measurements over a finite time-horizon. Thanks to this correction procedure the convergence of the width of the estimated State enclosure can be established. Through a numerical example, the performance of the proposed set-membership State Estimator are illustrated and compared to those of an ellipsoidal State Estimator.
-
ACC - Forward-Backward Set-Membership State Estimator Based on Interval Analysis
2018 Annual American Control Conference (ACC), 2018Co-Authors: Nacim Meslem, Nacim RamdaniAbstract:Interval computation is used to design a set-membership State Estimator for uncertain discrete-time linear systems. Based on the observability property, an output set-version technique is applied to discard all the parts of the predicted State enclosure that are inconsistent with system output measurements over a finite time-horizon. Thanks to this correction procedure the convergence of the width of the estimated State enclosure can be established. Through a numerical example, the performance of the proposed set-membership State Estimator are illustrated and compared to those of an ellipsoidal State Estimator.
S P Hoogendoorn - One of the best experts on this subject based on the ideXlab platform.
-
real time lagrangian traffic State Estimator for freeways
IEEE Transactions on Intelligent Transportation Systems, 2012Co-Authors: Yufei Yuan, J W C Van Lint, R E Wilson, F L M Van Wageningenkessels, S P HoogendoornAbstract:Freeway traffic State estimation and prediction are central components in real-time traffic management and information applications. Model-based traffic State Estimators consist of a dynamic model for the State variables (e.g., a first- or second-order macroscopic traffic flow model), a set of observation equations relating sensor observations to the system State (e.g., the fundamental diagrams), and a data-assimilation technique to combine the model predictions with the sensor observations [e.g., the extended Kalman filter (EKF)]. Commonly, both process and observation models are formulated in Eulerian (space-time) coordinates. Recent studies have shown that this model can be formulated and solved more efficiently and accurately in Lagrangian (vehicle number-time) coordinates. In this paper, we propose a new model-based State Estimator based on the EKF technique, in which the discretized Lagrangian Lighthill-Whitham and Richards (LWR) model is used as the process equation, and in which observation models for both Eulerian and Lagrangian sensor data (from loop detectors and vehicle trajectories, respectively) are incorporated. This Lagrangian State Estimator is validated and compared with a Eulerian State Estimator based on the same LWR model using an empirical microscopic traffic data set from the U.K. The results indicate that the Lagrangian Estimator is significantly more accurate and offers computational and theoretical benefits over the Eulerian approach.
Nacim Ramdani - One of the best experts on this subject based on the ideXlab platform.
-
Forward-Backward Set-Membership State Estimator Based on Interval Analysis
2018Co-Authors: Nacim Meslem, Nacim RamdaniAbstract:Interval computation is used to design a set- membership State Estimator for uncertain discrete-time linear systems. Based on the observability property, an output set-version technique is applied to discard all the parts of the predicted State enclosure that are inconsistent with systemoutput measurements over a finite time-horizon. Thanks to this correction procedure the convergence of the width of the estimated State enclosure can be established. Through a numerical example, the performance of the proposed set-membership State Estimator are illustrated and compared to those of an ellipsoidal State Estimator.
-
ACC - Forward-Backward Set-Membership State Estimator Based on Interval Analysis
2018 Annual American Control Conference (ACC), 2018Co-Authors: Nacim Meslem, Nacim RamdaniAbstract:Interval computation is used to design a set-membership State Estimator for uncertain discrete-time linear systems. Based on the observability property, an output set-version technique is applied to discard all the parts of the predicted State enclosure that are inconsistent with system output measurements over a finite time-horizon. Thanks to this correction procedure the convergence of the width of the estimated State enclosure can be established. Through a numerical example, the performance of the proposed set-membership State Estimator are illustrated and compared to those of an ellipsoidal State Estimator.
Kenji Doya - One of the best experts on this subject based on the ideXlab platform.
-
Reinforcement learning State Estimator.
Neural computation, 2007Co-Authors: Jun Morimoto, Kenji DoyaAbstract:In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-State estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to State estimation problems. Specifically, we derive a method to construct a nonlinear State Estimator by finding an appropriate feedback input gain using the policy gradient method. We tested the proposed method on single pendulum dynamics and show that the joint angle variable could be successfully estimated by observing only the angular velocity, and vice versa. In addition, we show that we could acquire a State Estimator for the pendulum swing-up task in which a swing-up controller is also acquired by reinforcement learning simultaneously. Furthermore, we demonstrate that it is possible to estimate the dynamics of the pendulum itself while the hidden variables are estimated in the pendulum swing-up task. Application of the proposed method to a two-linked biped model is also presented.
Ahmad Hably - One of the best experts on this subject based on the ideXlab platform.
-
Robust set-membership State Estimator against outliers in data
IET Control Theory and Applications, 2020Co-Authors: Nacim Meslem, Ahmad HablyAbstract:Based on interval computation, a set‐membership State Estimator capable to manage a certain type of outliers in measurements is proposed for uncertain discrete‐time linear systems. To achieve this purpose, two set‐valued filtering techniques are implemented in the presented State estimation algorithm. The setting up of these techniques offers two main advantages. On one hand, the convergence of the estimated State enclosures width is guaranteed and, on the other hand, the algorithm robustness against outliers in data is ensured. That is, unlike former methods, the proposed set‐valued State Estimator preserves the framing property despite the presence of some false values in available sensors data. To show the efficiency and the performance of the introduced set‐valued State Estimator, it is compared, through two numerical examples, with an optimal interval observer selected from the literature for its high performance.
