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Neural-network-based iterative learning control of nonlinear systems.
ISA Trans 2019IT

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.

Authors+Show Affiliations

Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland. Electronic address: k.patan@issi.uz.zgora.pl.Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland. Electronic address: m.patan@issi.uz.zgora.pl.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31493874

Citation

Patan, Krzysztof, and Maciej Patan. "Neural-network-based Iterative Learning Control of Nonlinear Systems." ISA Transactions, 2019.
Patan K, Patan M. Neural-network-based iterative learning control of nonlinear systems. ISA Trans. 2019.
Patan, K., & Patan, M. (2019). Neural-network-based iterative learning control of nonlinear systems. ISA Transactions, doi:10.1016/j.isatra.2019.08.044.
Patan K, Patan M. Neural-network-based Iterative Learning Control of Nonlinear Systems. ISA Trans. 2019 Sep 3; PubMed PMID: 31493874.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Neural-network-based iterative learning control of nonlinear systems. AU - Patan,Krzysztof, AU - Patan,Maciej, Y1 - 2019/09/03/ PY - 2019/02/13/received PY - 2019/08/13/revised PY - 2019/08/28/accepted PY - 2019/9/9/entrez PY - 2019/9/9/pubmed PY - 2019/9/9/medline KW - Convergence analysis KW - Iterative learning control KW - Neural networks KW - Nonlinear process JF - ISA transactions JO - ISA Trans N2 - 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. SN - 1879-2022 UR - https://www.unboundmedicine.com/medline/citation/31493874/Neural-network-based_iterative_learning_control_of_nonlinear_systems L2 - https://linkinghub.elsevier.com/retrieve/pii/S0019-0578(19)30390-8 DB - PRIME DP - Unbound Medicine ER -
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