Robustness analysis tools for an uncertainty set obtained by prediction error identi"cation
نویسندگان
چکیده
This paper presents a robust stability and performance analysis for an uncertainty set delivered by classical prediction error identi"cation. This nonstandard uncertainty set, which is a set of parametrized transfer functions with a parameter vector in an ellipsoid, contains the true system at a certain probability level. Our robust stability result is a necessary and su$cient condition for the stabilization, by a given controller, of all systems in such uncertainty set. The main new technical contribution of this paper is our robust performance result: we show that the worst case performance achieved over all systems in such an uncertainty region is the solution of a convex optimization problem involving linear matrix inequality constraints. Note that we only consider single input}single output systems. 2001 Elsevier Science Ltd. All rights reserved.
منابع مشابه
Robustness analysis tools for an uncertainty set obtained by prediction error identification
This paper presents a robust stability and performance analysis for an uncertainty set delivered by classical prediction error identi cation. This nonstandard uncertainty set, which is a set of parametrized transfer functions with a parameter vector in an ellipsoid, contains the true system at a certain probability level. Our robust stability result is a necessary and suÆcient condition for the...
متن کاملPROCESS CONTROL - RELEVANT AND CLOSED - LOOP IDENTIFICATION Paul
The identi cation of dynamical systems on the basis of data, measured under closed-loop experimental conditions, is a problem which is highly relevant in many (industrial) applications. When using models as a basis for model-based robust control design both nominal models and model uncertainty bounds are required. In this paper it is shown how -in particularmodel uncertainty bounds can be obtai...
متن کاملComparing Di erent Approaches to Model Error Modeling in Robust Identi cation
Identi cation for robust control must deliver not only a nominal model, but also a reliable estimate of the uncertainty associated with the model. This paper addresses recent approaches to robust identi cation, that explicitly aim at separating contributions from the two main uncertainty sources: unmodeled dynamics and noise a ecting the data. In particular, the following methods are considered...
متن کاملOn robust control synthesis and analysis in a Hilbert space!
The motivation for this paper stems from the need to develop a uniform framework for addressing problems in identi!cation and robust control. System identi!cation for in!nite-dimensional Hilbert spaces has been addressed earlier by the authors. System identi!cation set in an Hilbert space results in uncertain models where the description of non-parametric error is typically a ball belonging to ...
متن کاملSECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...
متن کامل