Identification of Switched ARX Systems using an Iterative Weighted Least Squares Algorithm

This paper presents a new algorithm for the identification of a specific class of hybrid systems. Hybrid System identification is a challenging problem since it involves the estimation of discrete and continuous states simultaneously. Using the method known as product of errors, this problem could be formulated such that, the identification of continuous state to be independent of discrete state estimation. We propose a new iterative weighted least squares algorithm (IWLS) for the identification of switched auto regressive exogenous systems (SARX). In this method, the parameters of only one subsystem are updated at each iteration while the parameters of the other subsystems are assumed known. In the method, all four main parameters of hybrid systems, namely Subsystem degrees, Number of subsystems, Unknown parameters vector and switching signal are estimated. Simulation results shows that our proposed method has a good performance in identifying the subsystems parameters and switching signal. Also, the superiority of our algorithm is shown by modeling of two SARX systems.