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On the worst-case experimental design for constrained linear systems

Author(s):

M. Tanaskovic, L. Fagiano, M. Morari
Conference/Journal:

Automatica, vol. 50, no. 12, pp. 32913298
Abstract:

The problem of experiment design for constrained linear systems with multiple inputs is addressed. A parametric model of the system is considered. The presented theoretical results provide a guideline on how to design experiments that minimize the worst-case identification error, as measured by the radius of information of the set of feasible model parameters, calculated in any norm. In addition, it is shown that an alternative, simpler approach can be employed when input constraints are symmetric and the worst-case identification error is minimized in either $1$- or $\infty$-norm. For such cases, on the basis of the derived results, a computationally tractable algorithm for the experiment design is proposed. The presented results are valid for a general model representation, which admits the commonly used finite impulse response model as a special case. The features of the presented method are illustrated in a numerical example.

Year:

2014
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@Article { TanFag:2014:IFA_4962,
    author={M. Tanaskovic and L. Fagiano and M. Morari},
    title={{On the worst-case experimental design for constrained
	  linear systems}},
    journal={Automatica},
    year={2014},
    volume={50},
    number={12},
    pages={32913298},
    month=dec,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=4962}
}
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