Identification of a non-stationary system using the Multi-Model approach (original) (raw)

Nonlinear system identification using uncoupled state multiple-model approach

2006

Multiple-model approach is an interesting alternative and a powerful tool for modelling complex processes. This paper deals with the off-line identification of non-linear systems employing the multiple-model approach. We use an uncoupled state multiple-model in opposition to the classically used coupled state multiplemodel (Takagi-Sugeno). The use of this new mutiple-model structure reveals a new undesirable phenomenon, called unhooking, that deteriorates the quality of the obtained approximation. An original solution is proposed to avoid this phenomenon.

NONLINEAR SYSTEM IDENTIFICATION USING HETEROGENEOUS MULTIPLE MODELS

Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, among them heterogeneous multiple model is of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state spaces are not the same and consequently they can be of various dimensions. Thanks to this feature, the complexity of the submodels can be well adapted to the complexity of the nonlinear system introducing flexibility and generality in the modelling stage. This paper deals with the off-line identification of nonlinear systems based on heterogeneous multiple model. Three optimisation criteria (global, local and combined) are investigated to obtain the submodel parameters according to the expected modelling performances. Particular attention is paid to the potential problems encountered in the identification procedure with a special focus on an undesirable phenomenon called no output tracking effect. The origin of this problem is explained and an effective solution is suggested to overcome this problem in the identification task. The abilities of this model are finally illustrated via relevant identification examples showing the effectiveness of the proposed methods.

Linear Identification of a Steam Generation Plant

The paper examines the development of models of steam generation plant using linear identification techniques. The identification process is carried out using experimental data of a multi-input multi-output MIMO system representation. Various techniques of modeling and identification are applied considering the complete system as a MIMO model and studying the effect of all the inputs on individual in different cases. The model will also be studied as a SISO system considering one input and one output at a time.

Real-Time System Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification

Computer-Aided Civil and Infrastructure Engineering, 2015

In this article, a novel Bayesian real-time system identification algorithm using response measurement is proposed for dynamical systems. In contrast to most existing structural identification methods which focus solely on parametric identification, the proposed algorithm emphasizes also model class selection. By embedding the novel model class selection component into the extended Kalman filter, the proposed algorithm is applicable to simultaneous model class selection and parametric identification in the real-time manner. Furthermore, parametric identification using the proposed algorithm is based on multiple model classes. Examples are presented with application to damage detection for degrading structures using noisy dynamic response measurement.

An Overview of System Identification Methods and Applications Part I: Methods of System Identification and Dynamic Tests

Methods of system identification (SI) for structural dynamic systems are reviewed in this paper. This paper discusses the scope and results of recently completed joint-industry research project in Iran. The project concluded the several parts in case of system identification. Herein presented the first part of this p roject. The methods considered, (relevant to stationary and non-stationary random processes) are parametric and nonparametric methods that are covered almost all existing methods. On the other part of this paper, types of dynamic tests and data acquisitions are described. At the end part of the paper, history and forecasting horizon of system identification are shown. Notice that, about 150 different papers & references were studied to implement these methods. For the purpose of effective applications, techniques of system identification need to be developed. So, the aim of the presented paper is to review the history, methods of system identification, type of tests and theories in the short summary for complete prospect of the system identification. This paper presented into two parts.

An Overview of System Identification Methods and Applications Part II: Theory, Type of Tested Structures, History and Prospective of System Identification

Methods of system identification (SI) for structural dynamic systems are reviewed in this paper. This paper discusses the scope and results of recently completed joint-industry research project in Iran. The project concluded the several parts in case of system identification. Herein presented the first part of this p roject. The methods considered, (relevant to stationary and non-stationary random processes) are parametric and nonparametric methods that are covered almost all existing methods. On the other part of this paper, types of dynamic tests and data acquisitions are described. At the end part of the paper, history and forecasting horizon of system identification are shown. Notice that, about 150 different papers & references were studied to implement these methods. For the purpose of effective applications, techniques of system identification need to be developed. So, the aim of the presented paper is to review the history, methods of system identification, type of tests and theories in the short summary for complete prospect of the system identification. This paper presented into two parts.

IJERT-Modeling and Identification of Linear Systems from Input-Output Data

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/modeling-and-identification-of-linear-systems-from-input-output-data https://www.ijert.org/modeling-and-identification-of-linear-systems-from-input-output-data System Identification is the determination of the system model of a dynamic system based on measured input-output data. In this paper concentration is made on different aspects of system identification, different models, parameter estimation methods and model validation. Here it is assumed that all data is available at once i.e. the variability of the system is studied rather than doing real on-line calculations. Non-parametric method i.e. spectral analysis and parametric method i.e. Least squares and Instrumental Variable methods are used as process identification methods. Some of the recursive (on-line) algorithms are also studied. Simple demonstrations are performed to support these aspects

Time domain identification of non-linear systems

Proceedings of ISSE'95 - International Symposium on Signals, Systems and Electronics

A new method of non-linear system design, and non-linear subsystem modelling is presented. The method is based on identifying a non-linear model for each subsystem and combining the individual models to solve the entire system. Time domain modelling and simulation is used throughout the procedure but frequency domain characteristics are: also available. Modelling of non-linear subsystem is achieved by

Recent Advancements & Methodologies in System Identification: A Review

System Identification (SI) is a discipline in control engineering concerned with inferring mathematical models from dynamic systems based on its input/output observations. Rich literature is available regarding SI due to its applications in understanding complex systems as well as to design control systems for complex processes. A summary of those literatures is presented in this paper, which covers general classifications of SI, methodologies for design and implementation of the models, as well as recent advancements in application of optimization techniques for SI. It is hoped that this paper would serve as a guide for budding practitioners regarding the fundamentals of the discipline, while serving as a summary of new research trends for experienced researchers in the area.