Terminal iterative learning control for discrete-time nonlinear systems based on neural networks

DOI10.1016/J.JFRANKLIN.2018.03.008zbMath1390.93318OpenAlexW2790486475WikidataQ130102123 ScholiaQ130102123MaRDI QIDQ1643228

Chiang-Ju Chien, Jian Han, Dong Shen

Publication date: 19 June 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2018.03.008

zbMATH Keywords

nonlinear systemsneural networksradial basis function neural networkLyapunov like methodterminal iterative learning control

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) Nonlinear systems in control theory (93C10) Design techniques (robust design, computer-aided design, etc.) (93B51) Discrete-time control/observation systems (93C55)

Related Items (1)

Event-triggered state tracking for two-dimensional neural networks with impulsive learning control schemes

Cites Work

• Some remarks on a conjecture in parameter adaptive control
• Adaptive Terminal ILC for Iteration-varying Target Points
• Optimal Terminal Iterative Learning Control for the Automatic Train Stop System
• Terminal iterative learning control based station stop control of a train
• Stochastic high‐order internal model‐based adaptive TILC with random uncertainties in initial states and desired reference points

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