A kernel-based approach to errors-in-variables identification of stable multivariable linear systems

In this paper, we present a kernel-based non-parametric approach to identifying stable multi-input multi-output linear systems in the presence of bounded noise affecting both the input and the output measurements. Firstly, we formulate the considered problem in terms of robust optimization techniques. Then, we show that the formulated robust optimization problem can be solved using semidefinite optimization. Since the involved optimization problem is computationally demanding, we also provide a result that allows the user to compute a bound on the approximation error introduced by considering reduced complexity models. We present some simulation examples to show the effectiveness of the proposed approach. Finally, we apply the proposed identification method to the dataset experimentally collected on a linear electronic filter.