Identification of Nonlinear Systems Using a B-Splines Parametric Subspace Approach
San Diego, CA
Proceedings of the American Control Conference
ISSN or ISBN
System identification theory has benefited from developments in numerical linear algebra, in particular, generalizations and extensions of the singular value decomposition. Thanks to these new developments, a new class of algorithms collectively known as subspace algorithms has emerged. These algorithms have the advantage of working directly in the state-space domain, which makes them quite appealing for designing model-based controllers. Extensions to nonlinear systems have appeared for bilinear and Hammerstein systems. We introduce a B-splines subspace approach for identifying nonlinear systems. It is based on a parametric B-splines transformation of the inputs, followed by linear system identification. In this sense, our approach identifies a Hammerstein model with B-splines as the input basis. Since the inputs depend parametrically on the spline functions, an iterative procedure is developed for obtaining the optimal parameters. An example of a rainfall-runoff application is presented.
Ramos, Jose A. and Durand, J. F., "Identification of Nonlinear Systems Using a B-Splines Parametric Subspace Approach" (1999). CEC Faculty Articles. 389.