Iterative Learning Identification of Singular Nonlinear Systems Based on Vector Plot Analysis

This paper describes an iterative learning identification law for singular nonlinear systems based on vector plot analysis. Firstly, a new recognition learning law based on vector plot analysis is proposed for such systems running in finite time intervals, which is used to iteratively analyze time-varying system parameters. Secondly, through the contraction mapping principle, the learning convergence of identifier errors under the λ-norm along the iteration axis is illustrated. Finally, numerical simulations are conducted to verify the efficiency of the proposed vector plot-based iterative learning identification law.