Decomposition-based least squares parameter estimation algorithm for input nonlinear systems using the key term separation technique

Abstract

A decomposition-based recursive least squares algorithm is developed for estimating the parameters of the input nonlinear systems composed of a dynamic controlled autoregressive block following a static nonlinear function with the known basis. The basic idea is that introducing the key term separation technique to parameterize the input nonlinear system and employing the hierarchical identification principle to decompose the parameterized system into two fictitious subsystems whose parameter vectors are recursively estimated using the recursive least squares methods. The proposed algorithm avoids estimating the redundant parameters compared with the over-parameterization identification method and improves computational efficiency. The simulation results confirm the effectiveness of the proposed algorithm.