S. Tøffner-Clausen

Introduction to Estimation Theory

The use of system identification in control engineering has been a popular alternative to physical modeling for obtaining model descriptions of a given physical system. Application of the fundamental laws from mechanics, thermodynamics, chemistry etc. are often quite complex and time consuming tasks especially if large scale engineering systems are considered. In such cases, when plant input/output measurements are available, system identification provides an alternative for generating a model for use in control design. Usually, system identification involves linear discrete-time models. However, the general ideas are not restricted to such systems, but apply for non-linear continuous-time models as well. In this book, however, we will consider linear discrete-time models exclusively. Considering linear systems the obtained models will be readily applicable in connection with linear control systems design.

Proceedings of the American Control Conference, Albuquerque, New Mexico

A robust controller design for the coupled mass benchmark problem is presented. The applied design method is based on a modified D-K iteration, i.e. μ-synthesis which take care of mixed real and complex perturbations sets. This μ-synthesis method for mixed perturbation sets is a straightforward extension of the standard D-K iteration for complex perturbation sets.

Introduction to Robust Control

Design of controllers with guaranteed closed loop stability and performance for uncertain plants have been the focus of active research for almost 2 decades now. Most of the research on robust control has focused on H∞ like problems. However, it turns out that many practical problems do not readily fit the standard H∞ problem setup since the involved model uncertainty is structured rather than unstructured. An H∞ control design will then be potentially conservative and thus will limit the obtainable performance of the closed loop system.

Estimation of Frequency Domain Model Uncertainties with Application to Robust Controller Design

Abstract This paper deals with the combination of system identification and robust controller design. Recent results on estimation of frequency domain model uncertainty using stochastic embedding of the undermodelling are utilized in robust discrete-time controller design. A simple iterative design methodology for IMC compensation of SISO plants has been developed. The methodology has been successfully applied to a laboratory centrifugal pump configuration resembling a small domestic water supply system.

Mu Synthesis for the Coupled Mass Benchmark Problem

A robust controller design for the coupled mass benchmark problem is presented. The applied design method is based on a modified D-K iteration, i.e. μ-synthesis which take care of mixed real and complex perturbations sets. This μ-synthesis method for mixed perturbation sets is a straightforward extension of the standard D-K iteration for complex perturbation sets.

Classical System Identification

The purpose of this chapter is to provide an introduction to system identification using prediction error methods (PEM). For a general treatment of system identification refer to any good textbook on the subject, like the two classic books [Lju87, SS89]. Here we will treat only the PEM approach. Furthermore we shall concentrate on results which are relevant for our purpose, namely estimation of model uncertainty. Specifically, the asymptotic distribution of the parameter estimates will be investigated under different assumptions. The representation and notation generally follows [Lju87, Chap. 9]. The general black-box model structure

Mixed μ Control of a Compact Disc Servo Drive

A(q)y(k) = \frac{{B(q)}}{{F(q)}}u(k) + \frac{{C(q)}}{{D(q)}}e(k)

Orthonormal Filters in System Identification

(9.1) will be assumed with