Nonlinear Discrete-time System Identification without Persistence of Excitation: Finite-time Concurrent Learning Methods
Abstract
This paper deals with the problem of finite-time learning for unknown discrete-time nonlinear systems' dynamics, without the requirement of the persistence of excitation. Two finite-time concurrent learning methods are presented to approximate the uncertainties of the discrete-time nonlinear systems in an online fashion by employing current data along with recorded experienced data satisfying an easy-to-check rank condition on the richness of the recorded data which is less restrictive in comparison with persistence of excitation condition. For the proposed finite-time concurrent learning methods, rigorous proofs guarantee the finite-time convergence of the estimated parameters to their optimal values based on the discrete-time Lyapunov analysis. Compared with the existing work in the literature, simulation results illustrate that the proposed methods can timely and precisely approximate the uncertainties.
Cite
@article{arxiv.2112.07765,
title = {Nonlinear Discrete-time System Identification without Persistence of Excitation: Finite-time Concurrent Learning Methods},
author = {Farzaneh Tatari and Christos Panayiotou and Marios Polycarpou},
journal= {arXiv preprint arXiv:2112.07765},
year = {2022}
}