CDC2000 Modelling, Identification and Validation of Nonlinear Systems

Modelling, Identification and Validation of Nonlinear Systems
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	Comparison of Systems with Complex Behavior: Spectral Methods Authors: Igor Mezić, Andrzej Banaszuk, Volume: 1, Page 1224 Paper number 4801 Abstract: We present a formalism for comparing the asymptotic dynamics of dynamical systems with physical systems that they model. There is often no need for the detailed (trajectory-wise) comparison of a dynamical system and the physical system that it models, but only comparison in statistical sense. For that purpose, invariant measures are typically considered. But, invariant measures usually can not be observed directly in an experiment. Thus, we base our formalism on time-averages obtained from a single observable. In particular, we constructively prove that, generically, a single observable is needed in order to recover an invariant ergodic measure. Pseudometrics on space of dynamical systems can be defined using this formalism in order to compare their statistical behavior. We also identify the need to go beyond comparing only invariant ergodic measures of systems and introduce an ergodic-theoretic treatment of a class of spectral functionals that allow for this. The formalism is extended for a class of stochastic systems: discrete Random Dynamical Systems. The ideas introduced in this paper can be used for parameter identification and model validation of driven nonlinear models with complicated behavior. As an illustration we provide an example in which we compare the asymptotic behavior of a combustion system measured experimentally with the asymptotic behavior of the model that is a stochastic control dynamical system. CD004801.PDF (From Author) TOP Model Validation for Nonlinear Feedback Systems Authors: Roy S. Smith, Geir Dullerud, Scott A. Miller, Volume: 1, Page 1232 Paper number 4802 Abstract: Model validation provides a useful means of assessing the ability of a model to account for a specific experimental observation, and has application to modeling, identification and fault detection. In a robust control framework norm-bounded perturbations are included to account for dynamic uncertainties in the system. We consider a discrete-time or sampled-data framework with a general linear fractional transformation (LFT) model structure which allows for the consideration of nonlinear feedback structures. Block structured, causal , time-varying perturbations are considered and we give a sufficient condition---necessary and sufficient in the single perturbation block case---for the model to be invalidated by the datum. The condition is testable by a convex LMI feasibility problem in which the matrix basis grows linearly in size with respect to the data length and the number of decision variables is equal to the number of perturbation blocks. CD004802.PDF (From Author) TOP Validation Test Design For A Nonlinear Model Developed From Limit Cycle Data Authors: Wayne J. Dunstan, Robert R. Bitmead, Sergio M. Savaresi, Volume: 1, Page 1237 Paper number 4803 Abstract: This paper details an example of experiment design for validation of a nonlinear model arising in the limit cycling behavior of lean, premixed combustion. The validation problem is important because of the poor information content of the periodic limit cycle data and the challenge is to provide a practically feasible, small excitation to the loop to improve identifiability and to provide qualitative tests of model performance. We examine this problem by considering the nonlinear dynamics of the model class and feasible excitation mechanisms. CD004803.PDF (From Author) TOP Unfalsified Nonlinear Adaptive Control Authors: Robert L. Kosut, Volume: 1, Page 1243 Paper number 4804 Abstract: In this paper we pursue the idea that adaptive linear control can be used to nullify (possibly) deleterious effects of unknown nonlinearities. The design approach is based on ideas of direct controller falsification. An example is presented which shows control of a nonlinear systems which exhibits multi-periodic and chaotic behavior. CD004804.PDF (From Author) TOP On the Identifiability of Nonlinear Maps in a General Interconnected System Authors: Mareike Claassen, Eric Wemhoff, Andrew Packard, Kameshwar Poolla, Volume: 1, Page 1248 Paper number 4805 Abstract: In this paper we investigate the problem of identifying static nonlinear maps as part of a general, structured interconnected system. The static nonlinear maps that require identification are not naturally parameterized via basis functions or other expansions. Thus, we are interested in non-parametric identification of these nonlinear maps. The particular focus of this paper is the issue of ``identifiability''. Loosely speaking, the static nonlinear maps in an interconnected system are identifiable if it is possible to determine them uniquely on the basis of input-output experiments. As is well known, identifiability concepts are of fundamental importance in system identification. In this paper, we focus on the case where only the static nonlinearity needs to be identified and the linear components of the interconnection are known. We offer a readily computable test for identifiability in the case that the inputs to every nonlinear map are measured. This test reduces to a matrix positivity computation. CD004805.PDF (From Author) TOP Identification of an Univariate Function in a Nonlinear Dynamical Model Authors: Benoit David, Georges Bastin, Volume: 1, Page 1254 Paper number 4806 Abstract: This paper addresses the problem of estimating, from measurement data corrupted by highly correlated noise, the shape of an unknown scalar and univariate function hidden in a known phenomenological model of the system. The method makes use of the Vapnik's support vector regression to find the structure of a parametrized black box model of the unknown function. Then the parameters of the black box model are identified using a maximum likelihood estimation method specially well suited to cope with correlated noise. The ability of the method to provide an accurate confidence bound for the unknown function is clearly illustrated from a simulation example. CD004806.PDF (From Author) TOP