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- W2896735988 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." @default.
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- W2896735988 date "2018-07-01" @default.
- W2896735988 modified "2023-10-16" @default.
- W2896735988 title "New Results on Deterministic Learning of Sampled-Data Nonlinear Systems" @default.
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- W2896735988 doi "https://doi.org/10.23919/chicc.2018.8483544" @default.
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