CDC2000 Nonlinear Estimation and Filtering

Nonlinear Estimation and Filtering
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation Adaptive Control: Linear Systems Estimation and Closed Loop Identification Control of Markov Processes Nonlinear Filtering and Control Modelling, Identification and Validation of Nonlinear Systems Differential Geometric Control Theory for Mechanical Systems Nonlinear Output Feedback Control Pneumatics and Compression Systems Control of Quantum Phenomena II Stability of Hybrid Systems Performance Analysis in Communication Networks Adaptive Control of Nonlinear Systems LMI Methods in Design Robust Control of Time Delay Systems Subspace Identification Methods Nonlinear Stochastic Filtering and Estimation Bifurcations, Chaos and Control I New Progress in Synthesis of Nonlinear Systems I Implementation Issues of Sliding Mode Control Theory Control of Mixing in Shear Flows Novel Neural Network Control Techniques for Industrial Motion Control Systems Physiological Control Systems Optimal Control of Hybrid Systems Stochastic Models for Communication Networks Control and Stabilisation of Nonlinear Systems New Directions in Robust Control Linear Systems Theory Advanced Topics in Systems Theory Estimation in Action Bifurcations, Chaos and Control II New Progress in Synthesis of Nonlinear Systems II Numerical Design and Analysis Techniques for Nonlinear Systems Analysis and Control of Underactuated Systems Sliding Mode Control I Challenges in the Application of Control to Computer Systems Estimation and Diagnosis of Discrete Event Systems Communications and Games Optimal Control Stochastic Systems Model Reduction Methodologies Identification and Subspace Methods Applications of Nonlinear Adaptive Control Advances in Nonlinear Output Feedback Design The Behavioural Approach to Systems and Control Vision Based Estimation and Control: Recent Advances and Open Problems Agile Control of Military Operations Sliding Mode Control II Model-based Fault Diagnosis of Industrial Processes Discrete Event Systems / Petri Nets System Identification and Confidence Estimation New Approaches to H-Infinity Control I Probabilistic Approaches to Robust Control Time Delay System Stabilisation Identification Methods Controlled Stochastic Processes Output Feedback of Nonlinear Systems Topics in Nonlinear Stabilisation Mobile Robots: Tracking Control Robust Control of Nonlinear Systems Power Systems Stabilisation and Control Disk Drive Control Hybrid Control Applications Discrete Time Systems New Approaches to H-Infinity Control II Linear Systems with Saturating Actuators New Theories in Distributed Parameter Systems Applications of Estimation and Identification Stochastic Control and Tuning Methodologies Control of Nonlinear Systems Iterative Learning and Control Coordinating Robot Systems Nonlinear Time Varying Systems Novel Applications of Neural Networks Aerospace Applications Switched Systems Implicit and Descriptor Systems LQG Periodic Systems and Disturbances New Horizons for Distributed Parameter Systems State Estimation Learning and Neuro-Control Nonlinear Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Robust Continuous-Time Smoothers -- Without Two-Sided Stochastic Integrals Authors: Vikram Krishnamurthy, Robert J. Elliott, Volume: 1, Page 286 Paper number 2034 Abstract: We consider the problem of fixed-interval smoothing of a continuous-time partially observed nonlinear stochastic dynamical system. Existing results for such smoothers require the use of two sided stochastic calculus. The main contribution of this paper is to present a robust formulation of the smoothing equations. Under this robust formulation, the smoothing equations are non-stochastic parabolic partial differential equations (with random coefficients -- and hence the technical machinery associated with two sided stochastic calculus is not required. Furthermore, the robust smoothed state estimates are locally Lipschitz in the observations -- which is useful for numerical simulation. As an example, finite dimensional robust versions of the Hidden Markov Model smoothers are derived. CD002034.PDF (From Author) TOP Solution to Brockett's Problem on Finite-Dimensional Estimation Algebras of Maximal Rank in Nonlinear Filtering Authors: Stephen S.T. Yau, Volume: 1, Page 292 Paper number 1304 Abstract: The Kalman-Bucy filter is widely used in modern industry. Despite its usefulness, however, the Kalman-Bucy filter is not perfect. One of the weakness is that it needs a Gaussian assumption for the initial data. The other weakness is that it requires the drift term f(x) be a linear function. Brockett [Br], Brockett and Clark [Br-Cl], and Mitter [Mi] proposed independently using a Lie algebraic method to solve Duncan-Mortensen-Zakai equation for nonlinear filtering. This method requires only n sufficient statistics, where n is the state space dimension, and it allows the initial condition be modeled by an arbitrary distribution. The idea was worked out in detail by Tam-Wong-Yau [TWY] and Yau [Ya 1] [Ya 2]. However, in the Lie algebraic method, one has to know explicitly the structure of the estimation algebra. In 1983, Brockett proposed to classify all finite dimensional filters. In this paper, we report the recent results on classification of finite dimensional maximal rank estimation algebras with arbitrary state space dimension. CD001304.PDF (From Author) TOP On Estimators For Nonlinear Systems In (cal) L_p Spaces Authors: Angelo Alessandri, Marcello Sanguineti, Volume: 1, Page 298 Paper number 1900 Abstract: Estimation problems are addressed for continuous-time, nonlinear dynamic systems in a general (cal) L_p framework. In this setting, the connection between the observation and the filtering problems is investigated. Under some regularity assumptions for the nonlinearities and suitable bounds on the (cal) L_p norm of the noises, it is proved that the same hypotheses sufficient to design an exponential observer for a system without noises enable one to design a filter which is (cal) L -stable with respect to system and measurement noises. An illustrative example is finally presented. CD001900.PDF (From Author) TOP Monte Carlo Filtering on Lie Groups Authors: Alessandro Chiuso, Stefano Soatto, Volume: 1, Page 304 Paper number 1407 Abstract: We propose a nonlinear filter for estimating the trajectory of a random walk on a matrix Lie group with constant computational complexity. It is based on a finite-dimensional approximation of the conditional distribution of the state - given past measurements - via a set of fair samples, which are updated at each step and proven to be consistent with the updated conditional distribution. The algorithm proposed, like other Monte Carlo methods, can in principle track arbitrary distributions evolving on arbitrarily large state spaces. However, several issues concerning sample impoverishment need to be taken into account when designing practical working systems. CD001407.PDF (From Author) TOP Data Fusion Based State Estimation Of Nonlinear Discrete Systems Authors: Jae-Won Lee, Sukhan Lee, Dongmok Shin, Volume: 1, Page 310 Paper number 2057 Abstract: In this paper, we propose a geometric data fusion(GDF) method using Perception-Net which can provide error reducing, uncertainty management, and maintaining consistency. We propose a Perception-Net to design a new state estimator for dynamic systems and apply the proposed geometric data fusion method to obtain the optimal estimate, propagate uncertainties and utilize the system knowledge. We present comparisons between the proposed estimator and the conventional estimators. It is also shown that the additional priori information on the system can be easily utilized in the proposed estimator to improve the performance. Through illustrative examples, it is verified that the proposed estimator presents better performances than the existing filters and improves performances via utilizing system knowledge. CD002057.PDF (From Author) TOP Sliding Mode Observer for Uncertain Systems, Part I: Linear System Case Authors: Yi Xiong, Mehrdad Saif, Volume: 1, Page 316 Paper number 1221 Abstract: A new sliding mode observer for linear uncertain systems is proposed. The advantage of the proposed observer is that it works under much less conservative conditions than Wallcot and Zak's observer. In addition, we address the issue of estimating a function of the state as well as unknown inputs or structural uncertainties. Further, the idea is extended to a general class of nonlinear uncertain systems. Numerical examples are used to illustrate the validity of the proposed observer design strategy. CD001221.PDF (From Author) TOP Sliding Mode Observer for Uncertain Systems, Part II: Nonlinear System Case Authors: Yi Xiong, Mehrdad Saif, Volume: 1, Page 322 Paper number 1222 Abstract: A new sliding mode observer based on Wallcot and Zak's observer for linear uncertain systems was proposed in Part I of this article. The proposed observer works under much less conservative conditions than the ones previously proposed. In this article, the observer design methodology that was proposed for linear systems in the first part of this paper is extended to a general class of nonlinear uncertain systems. Numerical examples are used to illustrate the validity of the proposed observer design strategy. CD001222.PDF (From Author) TOP