CDC2000 Identification and Subspace Methods

Identification and Subspace Methods
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	Particle Filters For Recursive Model Selection In Linear And Nonlinear System Identification Authors: Visakan Kadirkamanathan, Mohamed H. Jaward, Simon G. Fabri, Maha Kadirkamanathan, Volume: 1, Page 2391 Paper number 2158 Abstract: Recursive model selection can be addressed within the Bayesian framework, the multiple model algorithm being one such approach for linear Gaussian systems. The recent advances in nonlinear non-Gaussian estimation with the sequential Monte Carlo algorithms such as the particle filter allow the application of Bayesian inference to the development of recursive model selection algorithms for general nonlinear non-Gaussian systems. Such an algorithm is developed in this paper and applied to a linear auto-regressive (AR) and nonlinear auto-regressive (NAR) systems. CD002158.PDF (From Author) TOP Blockwise Subspace Identification for Active Noise Control Authors: Rufus Fraanje, Michel Verhaegen, Niek J. Doelman, Volume: 1, Page 2397 Paper number 1600 Abstract: In this paper, a subspace identification solution is provided for Active Noise Control (ANC) problems. The solution is related to so-called block updating methods, where instead of updating the (feedforward) controller on a sample by sample base, it is updated each time based on a block of N samples. The use of the subspace identification based ANC methods enables non-iterative derivation and updating of MIMO compact state space models for the controller. The robustness property of subspace identification methods forms the basis of an accurate model updating mechanism, using small size data batches. The design of a feedforward controller via the proposed approach is illustrated for an acoustic duct benchmark problem, supplied by TNO Institute of Applied Physics (TNO-TPD), the Netherlands. We also show how to cope with intrinsic feedback. A comparison study with various ANC schemes, such as block Filtered-U demonstrates the increased robustness of a subspace derived controller. CD001600.PDF (From Author) TOP On Data Preprocessing for Subspace Methods Authors: Dietmar Bauer, Volume: 1, Page 2403 Paper number 9204 Abstract: In modern data analysis often the first step is to perform some data preprocessing, e.g. detrending or elimination of periodic components of known period length. This is normally done using least squares regression. Only afterwards black box models are estimated using either pseudo-maximum-likelihood methods, prediction error methods or subspace algorithms. In this paper it is shown, that for subspace methods this is essentially the same as including the corresponding input variables, e.g. a constant or a trend or a periodic component, as additional input variables. Here essentially means, that the estimates only differ through the choice of initial values. CD009204.PDF (From Author) TOP A Comparative Study On Subband Identification Authors: Damian Marelli, Minyue Fu, Volume: 1, Page 2409 Paper number 2024 Abstract: This paper reports some new study on the subband identification approach. We first provide a complete characterization for the subbands to be decoupled in the sense that different subband channels are statistically independent and the subband model is diagonal. We then apply this result to studying the performance of subband identification schemes based on recursive least-squares algorithms. We provide expressions for the computational cost, asymptotic residual error, and convergence rate. These expressions can be used to determine when the subband approach is advantageous over the full-band approach. A simulation example is given to demonstrate these advantages. CD002024.PDF (From Author) TOP The Relationship Between State Space Subspace Identification Methods And The EM Method Authors: Stuart Gibson, Brett Ninness, Volume: 1, Page 2415 Paper number 9105 Abstract: This paper exposes a close connection between subspace-type estimates and single iterates of the Expectation Maximisation (EM) method for Maximum Likelihood (ML) estimation. A key implication of this is that it suggests a means by which ML estimates for MIMO systems may be computed in a numerically robust and straightforward manner. CD009105.PDF (From Author) TOP Maximum Likelihood Estimation of Wiener Models Authors: Anna Hagenblad, Lennart Ljung, Volume: 1, Page 2417 Paper number 9117 Abstract: A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and output are measured, but not the intermediate signal. We discuss the Maximum Likelihood estimate for Gaussian measurement and process noise, and the special cases when one of the noise sources is zero. CD009117.PDF (From Author) TOP