New Results on Deterministic Learning of Sampled-Data Nonlinear Systems
Date of Original Version
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.
Chinese Control Conference, CCC
Wu, Weiming, Cong Wang, and Chengzhi Yuan. "New Results on Deterministic Learning of Sampled-Data Nonlinear Systems." Chinese Control Conference, CCC 2018-July, (2018): 1672-1677. doi:10.23919/ChiCC.2018.8483544.