A data-driven approach for the identification of nonlinear state-dependent switched systems using expectation-maximization

A maximum likelihood-based identification algorithm for nonlinear state-dependent switched systems is presented. The data-based modeling of switched systems is in principle more demanding, since assignments of the sampled recordings to their originating subsystems are not given. The resulting identification problem involves latent variables and is therefore solved by an expectation-maximization algorithm. The estimated likelihoods are used to construct the switching condition by a decision tree learning algorithm. The performance of the proposed method is demonstrated by two examples.