Title

New Results on Deterministic Learning of Sampled-Data Nonlinear Systems

Document Type

Conference Proceeding

Date of Original Version

10-5-2018

Abstract

In this paper, our main concern is to establish new exponential stability-based identification results for a class of Euler nonlinear sampled-data systems using deterministic learning. At first, a new deterministic learning law is designed based on the Lyapunov function method. Rigorous analysis is provided to show that the resulting closed-loop linear time-varying (LTV) systems (containing tracking errors and parameter estimation errors) is exponentially stable. All the states of the closed-loop system converge to a small neighborhood around the origin exponentially. Thus, locally-accurate identification performance can be achieved under the new deterministic learning algorithm. Finally, simulation results on Duffing oscillator system are given to show the effectiveness of the proposed method.

Publication Title

Chinese Control Conference, CCC

Volume

2018-July

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