Nonlinear Discrete-time Systems' Identification without Persistence of Excitation: A Finite-time Concurrent Learning

• URL: http://arxiv.org/abs/2112.07765v1
• Date: Tue, 14 Dec 2021 22:19:20 GMT
• Title: Nonlinear Discrete-time Systems' Identification without Persistence of Excitation: A Finite-time Concurrent Learning
• Authors: Farzaneh Tatari, Chiristos Panayiotou, Marios Polycarpou
• Abstract summary: A finite-time concurrent learning approach is presented to approximate the uncertainties of the discrete-time nonlinear systems. Rigorous proofs guarantee the finite-time convergence of the estimated parameters to their optimal values.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. A finite-time concurrent learning approach is presented to approximate the uncertainties of the discrete-time nonlinear systems in an on-line 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. Rigorous proofs guarantee the finite-time convergence of the estimated parameters to their optimal values based on a discrete-time Lyapunov analysis. Compared with the existing work in the literature, simulation results illustrate that the proposed method can timely and precisely approximate the uncertainties.

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