CDC2000 Identification Methods

Identification 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	Set Membership Identification of Nonlinear Systems Authors: Carlo Novara, Mario Milanese, Volume: 1, Page 2831 Paper number 2099 Abstract: In this paper we investigate the problem of finding upper and lower bounds of a real valued function of several variables, on the base of a set of noise corrupted values of the function evaluated at a given set of variables and on some assumptions on function regularity and on noise bounds. Several set membership linear and nonlinear identification problems can be recast into the above problem. Two solutions are proposed. The first one is quite straightforward and leads to the definition of bounds that are the tightest ones but, in high dimensional spaces, computationally expensive. The second solution, relying on approximation properties of neural networks, leads to the evaluation of somewhat more conservative bounds, whose computational complexity is significantly lower than for the optimal bounds. A numerical example, related to the identification and prediction of a Lorenz chaotic system, is presented to show the effectiveness of the proposed approach. CD002099.PDF (From Author) TOP Criterion For Selection Of Model And Controller Design Based On I/O Data Authors: Kouji Tsumura, Hidenori Kimura, Volume: 1, Page 2837 Paper number 2009 Abstract: In this paper, we propose information criteria not only for model estimation and selection but also for selection of design method of controller. The criteria are derived by the same approach of AIC/TIC, however we consider the modeling of the distribution of the control performance substituting for that of system index. The criteria are composed of a log-likelihood function and a bias term similar to AIC/TIC, and especially the bias term depends on the controller. Moreover, when the controller is designed under some conditions, the criteria become stochastic variables and their expectations are further added by the other bias terms, which are in proportion to the dimension of the control parameter. This shows that model estimation, selection and control designing are influenced simultaneously by the complexity of model and that of controller. CD002009.PDF (From Author) TOP Identification Of Systems With Direction-Dependent Dynamics Authors: H.A. Barker, Keith R. Godfrey, Ai Hui Tan, Volume: 1, Page 2843 Paper number 1265 Abstract: In this paper, the identification of systems with direction-dependent dynamics by means of bilinear models and Wiener models is considered. It is shown that when such systems are perturbed by pseudo-random binary signals based on maximum-length sequences, distinctive patterns are observed in the cross-correlation between the system input and system output. These patterns are not present when other kinds of pseudo-random binary signals are used. The patterns obtained for bilinear models and Wiener models are similar, and both depend on the characteristic polynomial of the maximum-length sequence used. For the case in which the dynamics involved are first-order, analytical results are obtained which allow the patterns to be compared in detail. The results expected when the pseudo-random signals used are inverse-repeat are also described. It is concluded that both kinds of model are suitable for use in this application, provided that the model parameters are appropriately chosen. CD001265.PDF (From Author) TOP Identification of Multivariable Hammerstein Systems Using Rational Orthonormal Bases Authors: Juan C. Gómez, Enrique Baeyens, Volume: 1, Page 2849 Paper number 1755 Abstract: In this paper, a non iterative algorithm for the simultaneous identification of the linear and nonlinear parts of multivariable Hammerstein systems is presented. The proposed algorithm is numerically robust, since it is based only on least squares estimation and singular value decomposition. Under weak assumptions on the persistency of excitation of the inputs, the algorithm provides consistent estimates even in the presence of coloured noise. Key in the derivation of the results is the use of rational orthonormal bases for the representation of the linear part of the system. An additional advantage of this is the possibility of incorporating prior information about the system in a typically black-box identification scheme. CD001755.PDF (From Author) TOP Asymptotic Properties of Hammerstein Model Estimates Authors: Dietmar Bauer, Brett Ninness, Volume: 1, Page 2855 Paper number 1750 Abstract: This paper considers the estimation of Hammerstein models. The main result of the paper lies in a specification of a set of sufficient conditions on the input sequence, the noise (and the true system) in order to ensure that a non-linear least-squares approach enjoys properties of consistency and asymptotic normality and furthermore, that an estimate of the parameter covariance matrix is also consistent. The set of assumptions is specified using the concept of near epoch dependence, which has been developed in the econometrics literature. Indeed, one purpose of this paper is to highlight the usefulness of this concept in the context of analysing estimation procedures for nonlinear dynamical systems. This setup is utilized in an example, where the static nonlinearity is due to input saturation. CD001750.PDF (From Author) TOP Recurrent Neural Networks For Identification Of Nonlinear Systems Authors: Xuemei Ren, Shumin Fei, Volume: 1, Page 2861 Paper number 109 Abstract: A new type of recurrent neural network is discussed in this paper, which provides the potential for the modelling of unknown nonlinear systems with multi-inputs and multi-outputs. The proposed network is a generalization of the network described by Elman. It is shown that the proposed network with appropriate neurons in the context layer can model unknown nonlinear systems. Based on the PID-like training objective function, the learning algorithm of the proposed network is considerably faster through the introduction of dynamic backpropagation, which is used to estimate the weights of both the feedforward and feedback connections. The techniques have been successfully applied to the modelling nonlinear plants and simulation results are included. CD000109.PDF (From Author) CD000109.PDF (Scanned) TOP Frequency Domain Curve Fitting: A Global Approach Authors: Olav Slupphaug, Volume: 1, Page 2867 Paper number 1157 Abstract: We consider the problem of fitting fixed order rational SISO transfer functions to frequency response data using a least squares criterion. The main contribution is showing how this problem can be solved semi-globally by transforming it into a bilinear matrix inequality (BMI) problem. The BMI problem is in turn solved by a branch-and-bound approach. An application is given to 'linearization' of a complex dynamic multi-phase flow simulator. CD001157.PDF (From Author) TOP