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

The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is uti...

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Bibliographic Details
Published inJournal of the Franklin Institute Vol. 355; no. 8; pp. 3641 - 3658
Main Authors Han, Jian, Shen, Dong, Chien, Chiang-Ju
Format Journal Article
LanguageEnglish
Published Elmsford Elsevier Ltd 01.05.2018
Elsevier Science Ltd
Subjects
Algorithms
Basis functions
Computer simulation
Control systems
Discrete element method
Discrete time systems
Error compensation
Errors
Iterative methods
Neural networks
Nonlinear control
Nonlinear systems
Radial basis function
Transportation terminals
Upgrading
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Summary:The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms.
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2018.03.008