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Related Experiment Videos

Learning feedforward control using a dilated B-spline network: frequency domain analysis and design.

YangQuan Chen1, Kevin L Moore, Vikas Bahl

  • 1Center for Self-Organizing and Intelligent Systems , Department of Electrical and Computer Engineering, Utah State University, Logan, UT 84322-4160, USA. yqchen@ece.usu.edu

IEEE Transactions on Neural Networks
|September 24, 2004
PubMed
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This study introduces a learning feedforward controller (LFFC) that enhances existing feedback controllers. The LFFC uses a dilated B-spline network for iterative signal updates, simplifying tuning with only two parameters.

Area of Science:

  • Robotics
  • Control Systems Engineering
  • Machine Learning

Background:

  • Existing control systems often rely solely on feedback controllers.
  • Iterative learning control offers potential for improved performance in repetitive tasks.
  • B-spline networks provide a flexible framework for function approximation in control.

Purpose of the Study:

  • To present a frequency-domain analysis and design method for a learning feedforward controller (LFFC).
  • To integrate the LFFC as an add-on to existing feedback controllers (FBC).
  • To investigate the use of a dilated B-spline network within the LFFC architecture.

Main Methods:

  • Frequency-domain analysis for controller design.
  • Iterative update of the LFFC signal based on previous feedback controller signals.

Related Experiment Videos

  • Utilizing a dilated B-spline network for the LFFC.
  • Stability analysis to derive design formulae.

    • Main Results:

    • The LFFC effectively supplements existing feedback controllers.
    • The B-spline dilation's impact on performance is analyzed.
    • Simplified tuning with only two parameters: B-spline support width and learning gain.
    • Simulation results demonstrate successful path tracking control for a wheeled mobile robot.

    Conclusions:

    • The proposed LFFC, based on a dilated B-spline network, offers a systematic approach to enhance control system performance.
    • The design is robust, supported by stability analysis and validated through simulations.
    • This method provides a practical and tunable solution for improving repetitive task control in systems like mobile robots.