Auxiliary Model Based Least Squares Iterative Algorithms for Parameter Estimation of Bilinear Systems Using Interval-Varying Measurements

This is the authors' abstract. We don't add key points for this paper.

This paper focuses on the parameter estimation of a class of bilinear systems, for which the input-output representation is derived by eliminating the state variables in the systems. Based on the obtained identification model and the hierarchical identification principle, a hierarchical auxiliary model based least squares iterative algorithm is derived, to improve the computation efficiency and the parameter estimation accuracy by using the auxiliary model identification idea and the interval-varying input-output data. For comparison, an auxiliary model based least squares iterative algorithm is presented. The simulation results show that the proposed algorithm has better performance in estimating the parameters of bilinear systems.