Identification of 2D Roesser models by using linear fractional transformations

In this paper, the problem of identifying a 2D linear time-invariant Roesser model is tackled. Based on the strong relation between the linear fractional representation and the nD Roesser model, a gradient-based optimization algorithm is suggested to estimate the state-space matrices of a standard Roesser model in the black-box as well as the gray-box model identification frameworks. Contrary to the developments available in the literature, no specific restriction (to the 2D causal, recursive and separable-in-denominator (CRSD) state-space models) is required by the non-linear programming technique developed in this article. The efficiency of this method is illustrated through two simulation examples: a CRSD state-space model and a 2D Roesser model of a co-current flow heat exchanger.