Abstract
Robustness against model uncertainty is essential in model-based controller design. It is well known that a relatively small uncertainty in lightly damped poles and zeros can result in a large distance measured in the $\nu $ -gap metric, leading to conservative robust stability and performance guarantees. This paper aims to develop an identification and control procedure that results in less conservative robust stability and performance conditions for linear systems with lightly damped poles and zeros. To achieve this, a connection is established between a distance measure based on a nonnormalized coprime factorization of the system and existing identification criteria in closed-loop system identification. A nominal model of the system is determined by minimizing this distance measure by means of a frequency-domain identification algorithm. Then, a controller synthesis method is proposed that addresses both nominal performance as robust stability. Improved robustness by using the proposed approach compared to existing approaches is confirmed in an experimental example for a system with lightly damped poles and zeros.
Highlights
R OBUSTNESS against model uncertainty is essential in feedback control, and as a consequence, determining the extent of model uncertainty is necessary for the associated modeling technique
One of the aims of this paper is to develop an identification for control approach that is tailored to the general distance measure framework
An identification and control procedure is developed within the general distance measure framework
Summary
R OBUSTNESS against model uncertainty is essential in feedback control, and as a consequence, determining the extent of model uncertainty is necessary for the associated modeling technique. In identification for control, the only purpose of the identified linear model Pis to design a high-performance controller C. When implementing the controller C on the true (linear) system P0, stability and performance cannot be guaranteed based on only the nominal model. Manuscript received February 28, 2017; revised January 29, 2018 and June 22, 2018; accepted October 14, 2018. Manuscript received in final form October 16, 2018. All research data supporting this publication are directly available within this publication.
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