Neural-network-based iterative learning control of nonlinear systems.

Clicks: 105
ID: 42533
2019
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Abstract
This work reports on a novel approach to effective design of iterative learning control of repetitive nonlinear processes based on artificial neural networks. The essential idea discussed here is to enhance the iterative learning scheme with neural networks applied for controller synthesis as well as for system output prediction. Consequently, an iterative control update rule is developed through efficient data-driven scheme of neural network training. The contribution of this work consists of proper characterization of the control design procedure and careful analysis of both convergence and zero error at convergence properties of the proposed nonlinear learning controller. Then, the resulting sufficient conditions can be incorporated into control update for the next process trial. The proposed approach is illustrated by two examples involving control design for pneumatic servomechanism and magnetic levitation system.
Reference Key
patan2019neuralnetworkbasedisa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Patan, Krzysztof;Patan, Maciej;
Journal ISA transactions
Year 2019
DOI
S0019-0578(19)30390-8
URL
Keywords Keywords not found

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