The Experts below are selected from a list of 4602 Experts worldwide ranked by ideXlab platform
Jie Huang - One of the best experts on this subject based on the ideXlab platform.
-
Cooperative output regulation of linear multi-agent systems subject to an uncertain leader system*
International Journal of Control, 2019Co-Authors: Shimin Wang, Jie HuangAbstract:ABSTRACTIn this paper, we study the cooperative output regulation problem of linear heterogeneous multi-agent systems subject to an uncertain leader system. A key component of our control law is the adaptive distributed observer for an uncertain leader system proposed recently by ourselves. We first show that this adaptive distributed observer is capable of estimating the Unknown Parameter Vector of the leader system exponentially as long as the leader's signal is persistently exciting. Then, we further solve our problem by both distributed state feedback control law and distributed measurement output feedback control law utilising the adaptive distributed observer.
-
ICIA - Cooperative Output Regulation of Linear Multi-Agent Systems subject to an Uncertain Leader System
2018 IEEE International Conference on Information and Automation (ICIA), 2018Co-Authors: Shimin Wang, Jie HuangAbstract:In this paper, we study the cooperative output regulation problem (CORP) of linear heterogeneous multi-agent systems subject to an uncertain leader system. A key component of our control law is the adaptive distributed observer (ADO) for an uncertain leader system proposed recently by ourselves. We first show that this ADO is capable of estimating the Unknown Parameter Vector of the leader system exponentially fast as long as the leader’s signal is persistently exciting. Then, we further solve our problem by a distributed measurement output (DMO) feedback control law utilizing the ADO.
-
Global adaptive output regulation for a class of nonlinear systems with iISS inverse dynamics using output feedback
Automatica, 2013Co-Authors: Jie Huang, Zhong-ping JiangAbstract:Abstract This paper generalizes our recent results on global robust output regulation of output feedback systems with integral input-to-state stable (iISS) inverse dynamics in two aspects. First, we consider the general relative degree case instead of the unity relative degree case. For this purpose, the one step approach proposed previously has to be generalized to a recursive approach. Second, we allow the exosystem to be uncertain. For this purpose, we need to introduce an adaptive control technique to estimate the Unknown Parameter Vector in the exosystem. A convergence analysis of the estimated Parameter Vector will also be given.
-
Global output regulation for output feedback systems with an uncertain exosystem and its application
International Journal of Robust and Nonlinear Control, 2010Co-Authors: Jie HuangAbstract:This paper presents the solvability conditions for the global robust output regulation problem for a class of output feedback systems with an uncertain exosystem by using output feedback control. An adaptive control technique is used to handle the Unknown Parameter Vector in the exosystem. It is shown that this Unknown Parameter Vector can be exactly estimated asymptotically if a controller containing a minimal internal model is employed. The effectiveness of our approach has been illustrated by an asymptotic tracking problem of a generalized fourth-order Lorenz system. Copyright © 2009 John Wiley & Sons, Ltd.
-
CDC - Output regulation for output feedback systems with an uncertain exosystem
Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009Co-Authors: Jie HuangAbstract:This paper studies the global robust output regulation problem for a class of output feedback systems subject to an uncertain exosystem by using output feedback control. An adaptive control technique is used to handle the Unknown Parameter Vector in the exosystem. It is shown that this Unknown Parameter Vector can be exactly estimated asymptotically if the controller incorporates a minimal internal model. The effectiveness of our approach has been illustrated by an asymptotic tracking problem associated with a generalized fourth-order Lorenz system.
Jean-yves Tourneret - One of the best experts on this subject based on the ideXlab platform.
-
A Hybrid Lower Bound for Parameter Estimation of Signals With Multiple Change-Points
IEEE Transactions on Signal Processing, 2019Co-Authors: Lucien Bacharach, Mohammed Nabil El Korso, Alexandre Renaux, Jean-yves TourneretAbstract:Change-point estimation has received much attention in the literature as it plays a significant role in several signal processing applications. However, the study of the optimal estimation performance in such context is a difficult task since the Unknown Parameter Vector of interest may contain both continuous and discrete Parameters, namely the Parameters associated with the noise distribution and the change-point locations. In this paper, we handle this by deriving a lower bound on the mean square error of these continuous and discrete Parameters. Specifically, we propose a Hybrid Cramér-Rao-Weiss-Weinstein bound and derive its associated closed-form expressions. Numerical simulations assess the tightness of the proposed bound in the case of Gaussian and Poisson observations.
-
Nonlinear regression using smooth Bayesian estimation
2015Co-Authors: Abderrahim Halimi, Corinne Mailhes, Jean-yves TourneretAbstract:This paper proposes a new Bayesian strategy for the estimation of smooth Parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model Parameters is considered. The joint posterior distribution of the Unknown Parameter Vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the Unknown model Parameters/hyperParameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed Bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated Parameters.
-
nonlinear unmixing of hyperspectral images using a generalized bilinear model
IEEE Signal Processing Workshop on Statistical Signal Processing, 2011Co-Authors: Abderrahim Halimi, Yoann Altmann, Nicolas Dobigeon, Jean-yves TourneretAbstract:This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization of the accepted linear mixing model but also of a bilinear model recently introduced in the literature. Appropriate priors are chosen for its Parameters in particular to satisfy the positivity and sum-to-one constraints for the abundances. The joint posterior distribution of the Unknown Parameter Vector is then derived. A Metropolis-within-Gibbs algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the Unknown model Parameters. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.
-
nonlinear unmixing of hyperspectral images using a generalized bilinear model
IEEE Transactions on Geoscience and Remote Sensing, 2011Co-Authors: Abderrahim Halimi, Yoann Altmann, Nicolas Dobigeon, Jean-yves TourneretAbstract:Nonlinear models have recently shown interesting properties for spectral unmixing. This paper studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of the accepted linear mixing model but also of a bilinear model that has been recently introduced in the literature. Appropriate priors are chosen for its Parameters to satisfy the positivity and sum-to-one constraints for the abundances. The joint posterior distribution of the Unknown Parameter Vector is then derived. Unfortunately, this posterior is too complex to obtain analytical expressions of the standard Bayesian estimators. As a consequence, a Metropolis-within-Gibbs algorithm is proposed, which allows samples distributed according to this posterior to be generated and to estimate the Unknown model Parameters. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.
-
IGARSS (3) - Bayesian Estimation of Altimeter Echo Parameters
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Jerome Severini, Corinne Mailhes, Pierre Thibaut, Jean-yves TourneretAbstract:This paper studies a Bayesian algorithm for estimating the Parameters associated to Brown's model. The joint posterior distribution of the Unknown Parameter Vector (amplitude, epoch and significant wave height) associated with this model is derived. This posterior is too complex to obtain closed form expressions of the minimum mean square error and the maximum a posteriori estimators. We propose to sample according to this distribution using an hybrid Metropolis within Gibbs algorithm. The simulated samples are then used to estimate the Unknown Parameters of Brown's model. The proposed strategy provides better estimations than the standard maximum likelihood estimator at the price of an increased computational cost.
A. Tiano - One of the best experts on this subject based on the ideXlab platform.
-
Identification of underwater vehicles by a Lyapunov method
IFAC Proceedings Volumes, 2004Co-Authors: A. TianoAbstract:Abstract This paper deals with the identification of non linear multivariable models of underwater vehicles by using a novel Lyapunov-based method. The method operates in the continuous time domain and can be applied to non linear models that are linear with respect to an Unknown Parameter Vector. After an introduction, where the role of identification methods in the area of guidance and control of underwater vehicles is outlined, some mathematical models that are generally used for describing the dynamics of underwater vehicles are concisely presented. The main features of the identification method are then illustrated through a simulation example concerning the surge dynamics of an underwater vehicle.
-
Identification of Multivariable Models of Underwater Vehicles
IFAC Proceedings Volumes, 2003Co-Authors: A. Tiano, A. Pecoraro, A. ZirilliAbstract:Abstract This paper deals with the identification of multivariable models of underwater vehicles. The proposed method can be applied to non linear models that are linear with respect to the Unknown Parameter Vector. In order to cope with time-varying Parameters, a recursive version ofthe identification algorithm has been implemented. The validity of this algorithm is illustrated by an application to the identification ofthe longitudinal dynamics of a DSRV(Deep Submergence Rescue Vehicle).
G. Ray - One of the best experts on this subject based on the ideXlab platform.
-
Robust controller with state-Parameter estimation for uncertain networked control system
IET Control Theory & Applications, 2012Co-Authors: Arun Kumar Sharma, G. RayAbstract:This study presents the design of an adaptive Kalman filter for networked systems involving random ‘sensor delays, missing measurements and packet dropouts’. Two different adaptive filters are considered to estimate Unknown Parameter Vector associated with the system matrices and subsequently the estimation of state and Parameters of the system based on the minimisation of square of the output prediction error is adopted in bootstrap manner. An estimator-based robust controller design has been proposed for asymptotic stability of the system whose Parameters can vary within a known bound. The effectiveness of the designed algorithms is tested through a numerical example under different cases.
Zhengtao Ding - One of the best experts on this subject based on the ideXlab platform.
-
Brief Global output regulation of uncertain nonlinear systems with exogenous signals
Automatica, 2001Co-Authors: Zhengtao DingAbstract:The paper addresses the global output tracking of a class of uncertain output feedback systems affected by disturbances which are generated from a known exosystem. The system uncertainty is parametrized by a Unknown Parameter Vector, which is absorbed in the parametrization of disturbances. A controller based on the estimation of states and disturbances from the output is designed using backstepping to asymptotically track arbitrary trajectories and to guarantee the boundedness of all other variables. The proposed control algorithm provides an alternative with global tracking to structurally stable regulation of nonlinear output feedback systems. It can also be viewed as a generalization to adaptive control algorithms.
