Bilinear State Space Systems for Nonlinear Dynamical Modelling

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Verdult, Vincent and Verhaegen, Michel (2000) Bilinear State Space Systems for Nonlinear Dynamical Modelling. Theory in Biosciences, 119 (1). pp. 1-9. ISSN 1431-7613

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Abstract:We discuss the identification of multiple input, multiple output, discrete-time bilinear state space systems. We consider two identification problems. In the first case, the input to the system is a measurable white noise sequence. We show that it is possible to identify the system by solving a nonlinear optimization problem. The number of parameters in this optimization problem can be reduced by exploiting the principle of separable least squares. A subspace-based algorithm can be used to generate initial estimates for this nonlinear identification procedure. In the second case, the input to the system is not measurable. This makes it a much more difficult identification problem than the case with known inputs. At present, we can only solve this problem for a certain class of single input, single output bilinear state space systems, namely bilinear systems in phase variable form.
Item Type:Article
Copyright:© 2000 Elsevier
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Link to this item:http://purl.utwente.nl/publications/74262
Official URL:http://dx.doi.org/10.1078/1431-7613-00001
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