Brief paper: Identification and control: Joint input design and H∞ state feedback with ellipsoidal parametric uncertainty via LMIs

Authors:
Märta Barenthin;Håkan Hjalmarsson
Affiliations:
Automatic Control, School of Electrical Engineering, KTH, 100 44 Stockholm, Sweden;Automatic Control, School of Electrical Engineering, KTH, 100 44 Stockholm, Sweden
Venue:
Automatica (Journal of IFAC)
Year:
2008

Citing 6
Cited 3

System identification: theory for the user

System identification: theory for the user
Canonical correlation analysis, approximate covariance extension, and identification of stationary time series

Automatica (Journal of IFAC)
For model-based control design, closed-loop identification gives better performance

Automatica (Journal of IFAC)
Identification For Control: Optimal Input Design With Respect To A Worst-Case $\nu$-gap Cost Function

SIAM Journal on Control and Optimization
Least costly identification experiment for control

Automatica (Journal of IFAC)
On the choice of inputs in identification for robust control

Automatica (Journal of IFAC)

Identification for control of multivariable systems: Controller validation and experiment design via LMIs

Automatica (Journal of IFAC)
Variance error, interpolation and experiment design

Automatica (Journal of IFAC)
Input design as a tool to improve the convergence of PEM

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

One obstacle in connecting robust control with models generated from prediction error identification is that very few control design methods are able to directly cope with the ellipsoidal parametric uncertainty regions that are generated by such identification methods. In this contribution we present a joint robust state feedback control/input design procedure which guarantees stability and prescribed closed-loop performance using models identified from experimental data. This means that given H"~ specifications on the closed-loop transfer function are translated into sufficient requirements on the input signal spectrum used to identify the process. The condition takes the form of a linear matrix inequality.