Identification of switching systems using change detection technique in the subspace framework

Abstract

The paper describes an identification technique of switching system. The considered system is represented as a weighted sum of local models. To estimate the switching times, a change detection technique is applied. It provides the weights associated to the local models. The Markov parameters of these models are identified by a subspace method. This calculation can yield similar local models which are merged. The procedure of parameter identification an models merging is repeated until convergence. The performance of the approach is investigated on a simulation example.