Robust generation of LPV state-space models using a regularized ℌ2-cost

In this paper we present a regularization of an H2-minimization based LPV-model generation algorithm. Our goal is to take care of uncertainties in the data, and obtain more robust models when we have few data. We give an interpretation of the regularization, which shows that the regularization has connections to robust optimization and worst-case approaches. We present how to effectively calculate the original cost function and its gradient, and extend these ideas to the regularized cost function and its gradient. A few examples, illustrating effects of both uncertain and few data, are finally presented to show the validity of the regularization.