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

Krzysztof Patan1, Maciej Patan1

  • 1Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.

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|September 9, 2019
PubMed
Summary

This study introduces a novel artificial neural network (ANN) approach for iterative learning control in nonlinear systems. The method enhances control synthesis and prediction, ensuring convergence and zero error for repetitive processes.

Keywords:
Convergence analysisIterative learning controlNeural networksNonlinear process

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Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Nonlinear Systems

Background:

  • Repetitive nonlinear processes require advanced control strategies for high-precision operation.
  • Traditional iterative learning control (ILC) methods can struggle with complex nonlinear dynamics.
  • Artificial neural networks offer powerful tools for modeling and control synthesis.

Purpose of the Study:

  • To develop an effective iterative learning control (ILC) design for repetitive nonlinear processes.
  • To integrate artificial neural networks (ANNs) for controller synthesis and system output prediction within the ILC framework.
  • To analyze the convergence and zero-error properties of the proposed data-driven nonlinear learning controller.

Main Methods:

  • Enhancing the ILC scheme by incorporating ANNs for both controller synthesis and system output prediction.
  • Developing an iterative control update rule based on an efficient data-driven ANN training scheme.
  • Characterizing the control design procedure and analyzing convergence and zero-error properties.

Main Results:

  • A novel data-driven iterative learning control approach using ANNs was successfully developed.
  • The proposed nonlinear learning controller demonstrated effective convergence and zero-error at convergence properties.
  • Sufficient conditions for control updates were derived and validated.

Conclusions:

  • The integration of ANNs into ILC provides an effective method for controlling repetitive nonlinear processes.
  • The proposed approach offers a robust data-driven solution with guaranteed performance characteristics.
  • The method's efficacy is demonstrated through successful application to pneumatic servomechanism and magnetic levitation systems.