On the equivalence of least costly and traditional experiment design for control

Authors:
Cristian R. Rojas;Juan C. Agüero;James S. Welsh;Graham C. Goodwin
Affiliations:
School of Electrical Engineering and Computer Science, The University of Newcastle, NSW 2308, Australia;School of Electrical Engineering and Computer Science, The University of Newcastle, NSW 2308, Australia;School of Electrical Engineering and Computer Science, The University of Newcastle, NSW 2308, Australia;School of Electrical Engineering and Computer Science, The University of Newcastle, NSW 2308, Australia
Venue:
Automatica (Journal of IFAC)
Year:
2008

Citing 8
Cited 1

System identification: theory for the user

System identification: theory for the user
Robust and optimal control

Robust and optimal control
Mathematical properties of optimization problems defined by positively homgeneous functions

Journal of Optimization Theory and Applications
Identification For Control: Optimal Input Design With Respect To A Worst-Case $\nu$-gap Cost Function

SIAM Journal on Control and Optimization
Robust optimal experiment design for system identification

Automatica (Journal of IFAC)
Least costly identification experiment for control

Automatica (Journal of IFAC)
Brief Robustness analysis tools for an uncertainty set obtained by prediction error identification

Automatica (Journal of IFAC)
From experiment design to closed-loop control

Automatica (Journal of IFAC)

The cost of complexity in system identification: The Output Error case

Automatica (Journal of IFAC)

Quantified Score

Hi-index	22.15

Visualization

Abstract

In this paper we establish the equivalence between least costly and traditional experiment design for control. We consider experiment design problems for both open and closed loop systems. In open loop, equivalence is established for three specific cases, relating to different parametrisations of the covariance expression (i.e. finite and high order approximations) and model structure (i.e. dependent and independently parameterised plant and noise models). In the closed loop setting, we consider only finite order covariance expressions. H"~ performance specifications for control are used to determine the bounds on the covariance expression for both the open and closed loop cases.