CDC2000 Identification and Control around the World

Identification and Control around the World
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	Aspects on the Interpretation of Disturbances in System Identification Authors: Liang-Liang Xie, Lennart Ljung, Volume: 1, Page 668 Paper number 3601 Abstract: The paper contains a discussion about what results about the quality of an estimated model can be achieved, if no probabilistic assumptions are introduced. Several technical results that illustrate possibilities and difficulties are also given. CD003601.PDF (From Author) TOP Hierarchical and Integrated Algorithms: Comparison and Applications in Motion Estimation and Recognition Authors: Kun Huang, Panganamala R. Kumar, Volume: 1, Page 674 Paper number 3602 Abstract: In this paper, we study several issues in motion estimation and object recognition. First, we compare the performance of two hierarchical and integrated methods in motion estimation. Second, we address the use of a simulated annealing algorithm for object recognition. This algorithm is then adapted for vehicle identification. CD003602.PDF (From Author) TOP Persistence Of Excitation Properties For The Identification Of Time-Varying Systems Authors: Sergio Bittanti, Marco C. Campi, Lei Guo, Volume: 1, Page 680 Paper number 3603 Abstract: The identification of time varying parameters requires that a certain level of information is present in the data through time. Only in this case it is in fact possible to track the parameter variability and form a reliable estimate. This consideration has led to the introduction in the literature of a variety of persistence of excitation notions ranging from the deterministic ones (in the 80's) to more sophisticated stochastic definitions proposed in the last decade. This paper presents an overview of the existing stochastic excitation notions and discuss important issues like their necessity for tracking and their applicability in different contexts. It appears that the present state of the art is not completely satisfying in terms of completeness and generality of the available results. CD003603.PDF (From Author) TOP The Role of Parametrizations in Identification of Linear Systems Authors: Manfred Deistler, Volume: 1, Page 685 Paper number 3604 Abstract: In identification the problem is to attach to every string of data of the form (equation deleted) , a system from an a priori specified model class. Usually the model class is described by a space of free parameters. In the fully automatized case, the system (or its free parameters) is attached to the data by a function, (equation deleted) say. If the data are assumed to be generated by an underlying stochastic process (called the data generating process, DGP) and if is measurable, then (equation deleted) is an estimator and the identification problem is an estimation problem. The special features of system identification arise from the rather complicated relation between external behavior, internal system parameters and free parameters for a given model class. CD003604.PDF (From Author) TOP Controller Validation For Stability And Performance Based On A Frequency Domain Uncertainty Region Obtained By Stochastic Embedding Authors: Xavier Bombois, Michel Gevers, Gérard Scorletti, Volume: 1, Page 689 Paper number 3605 Abstract: This paper presents a robustness analysis for an uncertainty set deduced from stochastic embedding techniques and made up of ellipsoids at each frequency in the Nyquist plane. Our robustness analysis focuses on the validation of a controller both for robust stability and for robust performance, over all systems in such frequency domain uncertainty region. Our validation procedure for stability ensures that the controller stabilizes all systems in this nonstandard uncertainty set. Our validation procedure for performance computes the worst case performance over all closed loop systems made up of the controller and all plants in the frequency domain uncertainty region. CD003605.PDF (From Author) TOP Stabilizability, Uncertainty and the Choice of Sampling Rate Authors: Feng Xue, Lei Guo, Volume: 1, Page 695 Paper number 3606 Abstract: We study in this paper a class of first-order continuous-time control systems with unknown nonlinear structure and with prescribed sampling rate h, aiming at understanding how stabilizability depends quantitatively upon the choice of the sampling rate and the "size" of the uncertainty. We show that if the unknown nonlinear function has a linear growth rate with its "slope" (denoted by L) being a measure of the"size" of uncertainty, then there exists a constant b (approx. 7.53) such that the system is not globally stabilizable by sampled-data feedback whenever Lh > b. Furthermore, if the unknown nonlinear function has a growth rate faster than linear, and if the system is disturbed by noises modeled as the standard Brownian motion, then an example is given, showing that the corresponding sampled-data system is not stabilizable in general, no matter how fast the sampling rate is. CD003606.PDF (From Author) TOP Closed-Loop Output Error Identification Of Nonlinear Plants Using Kernel Representations Authors: Franky De Bruyne, Brian D.O. Anderson, Ioan Dore Landau, Volume: 1, Page 700 Paper number 3607 Abstract: In this paper, we extend a family of algorithms for the identification of continuous time nonlinear plants operating in closed-loop. The main novelty is that the identification of unstable plants is covered in its generality. CD003607.PDF (From Author) TOP