We examine some recent methods for System Identification, that also deliver non-parametric error bounds, suited for a robust controller design. In particular, we look at Stochastic Embedding, Set Membership Identification and Model Error Modelling. We briefly review the main ideas together with existing computational solutions and present a comparative example.

The performance of robust controllers hinges on the underlyingmodel set. The aim of the present paper is to develop a system identification procedure that enables the design of a controller that achieves optimal robust performance. Hereto, the complex interrelation between system identification and robust control is thoroughly analyzed and novel connections are established between (i) control-r...

A new approach is given for the design of adaptive robust control in the frequency domain. Starting with an initial model of a stable plant and a robust stabilizing controller, the new (windsurfer) approach allows the bandwidth of the closed-loop system to he increased progressively through an iterative controlrelevant system identification and control design procedure. The method deals with bo...

This paper introduces a new robust-control-oriented system identification method, which consists of the following three steps: 1. High-order ARX model identification; 2. Loop shaping weighting functions design based on the high-order ARX model; 3. Control-oriented model reduction by minimizing the weighted L2-gap between the high-order ARX model and the low-order model. This method truly integr...

Various techniques of system identification exist providing for a nominal model and uncertainty bound. An important question is what the implications are for the particular choice of the structure in which the uncertainty is described when dealing with robust stability/performance analysis of a given controller and when dealing with robust synthesis. An amplitude-bounded (circular) uncertainty ...

In this paper a framework for connecting system identi cation and robust control design via a factorization approach is being introduced. Within the scope of this framework, models are represented in an estimated set of models that is used subsequently to design a robust performing controller. The set of models is structured by means of a nominal coprime factorization along with an allowable pe...

applying robust optimization to solve product mix problem in automotive industries in this article, applying robust optimization in product mix problem in automotive industries is presented. the problem includes determining both the quantity and the identification of each product to produce to maximize profit. at first, the deterministic model will be presented. then, robust model of product mi...

We propose a model validation procedure that consists of a prediction error identi6cation experiment with a full order model. It delivers a parametric uncertainty ellipsoid and a corresponding set of parameterized transfer functions, which we call prediction error (PE) unceminty set. Such uncertainty set differs from the classical uncertainty descriptions used in robust control analysis and des...