Hierarchical least squares parameter estimation algorithms for dual-rate sampled-data systems

In this paper, we combine the hierarchical identification principle with the least square algorithm to identify the parameters of dual-rate sampled-data systems. The hierarchical identification principle is to decompose the identification model of dual-rate systems to several identification sub-models with smaller dimensions and fewer parameters to be estimated, and to present the hierarchical least squares identification algorithm with less computation efforts. We prove the convergence of the algorithm proposed. The simulation example is included.