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

The terminal iterative learning control (ILC) is designed for discrete-time nonlinear system based on neural networks. A terminal output tracking error model is derived by using a system input and output algebraic function as well as the differential mean value theorem. The weight is updated by opti...

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Bibliographic Details
Published in2015 34th Chinese Control Conference (CCC) pp. 3190 - 3195
Main Authors Han, Jian, Shen, Dong, Chien, Chiang-Ju
Format Conference Proceeding Journal Article
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2015
Subjects
Algorithm design and analysis
Artificial neural networks
Computer simulation
Control systems
Convergence
Dynamical systems
Iterative Learning Control
Iterative methods
Mathematical analysis
Mathematical models
Neural Networks
Nonlinear dynamics
Nonlinear System
Nonlinear systems
Robustness
Terminals
Trajectory
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Summary:The terminal iterative learning control (ILC) is designed for discrete-time nonlinear system based on neural networks. A terminal output tracking error model is derived by using a system input and output algebraic function as well as the differential mean value theorem. The weight is updated by optimizing an optimal objective function, and then is used for the input design. The technical convergence analysis and numerical simulations are given for the fixed input case. Further discussions on time-varying input case and random iteration-varying initial condition are also given in illustrative simulations.
Bibliography:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:2161-2927
1934-1768
DOI:10.1109/ChiCC.2015.7260132