Bilinear state space systems for nonlinear dynamical modelling |
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Authors: | Vincent Verdult Michel Verhaegen |
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Affiliation: | (1) Faculty of Applied Physics, University of Twente, P.O. Box 217, NL-7500 AE Enschede, The Netherlands |
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Abstract: | Summary 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. |
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Keywords: | billinear systems system identification nonlinear dynamics modelling |
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