Abstract
This paper surveys recently proposed approaches for the model reduction of certain classes of uncertain, multi-dimensional, parameter varying and non-linear systems. It is shown that each of these systems may be written using a similar formulation. Balanced truncation model reduction, based on the solution of two Linear Matrix Inequalities (LMI’s) are discussed for each class of system and the similarities (and differences) highlighted.
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This paper is dedicated with warm friendship to Bruce Francis and Mathukumalli Vidyasagar on the occasion of their 50th birthdays.
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© 1999 Springer-Verlag London Limited
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Glover, K., Goddard, P.J., Chu, Y.C. (1999). Model reduction for classes of uncertain, multi-dimensional, parameter varying and non-linear systems. In: Yamamoto, Y., Hara, S. (eds) Learning, control and hybrid systems. Lecture Notes in Control and Information Sciences, vol 241. Springer, London. https://doi.org/10.1007/BFb0109734
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DOI: https://doi.org/10.1007/BFb0109734
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