A new neural network-based adaptive ILC for nonlinear discrete-time systems with dead zone scheme

DOI10.1007/S11424-009-9176-4zbMath1193.93130OpenAlexW2031783946MaRDI QIDQ967991

Zhongsheng Hou, Ronghu Chi

Publication date: 3 May 2010

Published in: Journal of Systems Science and Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11424-009-9176-4

zbMATH Keywords

adaptive controlneural networkiterative learning controlnon-identical initial conditionnon-identical trajectory

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) Adaptive control/observation systems (93C40) Discrete-time control/observation systems (93C55)

Related Items (3)

A data-driven adaptive ILC for a class of nonlinear discrete-time systems with random initial states and iteration-varying target trajectory ⋮ Adaptive iterative learning control for <scp>2D</scp> nonlinear systems with nonrepetitive uncertainties ⋮ Multi-lagged-input iterative dynamic linearization based data-driven adaptive iterative learning control

Cites Work

• Analysis of iterative learning control for a class of nonlinear discrete-time systems
• Multilayer feedforward networks are universal approximators
• Iterative learning control. Convergence, robustness and applications
• Adaptive control of linearizable systems
• Adaptive regulation of nonlinear systems with unmodeled dynamics
• Adaptive control of nonlinear systems using neural networks
• Adaptive output-feedback control of systems with output nonlinearities
• Adaptive control of a class of nonlinear discrete-time systems using neural networks

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