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Worst-case experiment design for constrained MISO systems


M. Tanaskovic, L. Fagiano, M. Morari

IEEE Conference on Decision and Control, Los Angeles, California, USA, vol. 53

The problem of optimal worst-case experiment design for constrained linear systems with multiple inputs represented by a parametric model is addressed. A theoretical result is derived, which provides an insight on how to design experiments that minimize the worst-case identification error in infinity and one norm when the input constraints are symmetric. The presented result is valid for a general model parametrization that admits the commonly used finite impulse response model as a special case. Based on this result a computationally tractable algorithm for the worst-case experiment design is proposed. Its advantages over a more standard experiment design approach are illustrated in a numerical example.


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% Autogenerated BibTeX entry
@InProceedings { TanFag:2014:IFA_4961,
    author={M. Tanaskovic and L. Fagiano and M. Morari},
    title={{Worst-case experiment design for constrained MISO systems}},
    booktitle={IEEE Conference on Decision and Control},
    address={Los Angeles, California, USA},
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