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A neuro-genetic controller for nonminimum phase systems.

S Park1, L J Park, C H Park

  • 1Dept. of Electr. Eng., Korea Adv. Inst. of Sci. and Technol., Taejon.

IEEE Transactions on Neural Networks
|January 1, 1995
PubMed
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This study introduces a novel neuro-genetic controller for nonminimum phase systems. The proposed controller demonstrates superior adaptation and performance improvements, including faster settling times and reduced overshoot, compared to traditional PID controllers.

Area of Science:

  • Control Systems Engineering
  • Computational Intelligence
  • Nonlinear Dynamics

Background:

  • Nonminimum phase systems present significant control challenges due to inherent instability.
  • Conventional controllers like PID may struggle with complex dynamics and achieving optimal performance.
  • Neuro-controllers offer adaptive capabilities but require effective training methodologies.

Purpose of the Study:

  • To develop and evaluate a hybrid neuro-controller for nonminimum phase systems.
  • To integrate a neuro-controller trained via genetic algorithm (GA) with a proportional plus integral plus derivative (PID) controller.
  • To assess the controller's performance in terms of adaptation and step response characteristics.

Main Methods:

  • A neuro-controller was designed for nonminimum phase systems.

Related Experiment Videos

  • The neuro-controller was trained offline using a genetic algorithm (GA).
  • The trained neuro-controller was combined in parallel with a conventional PID controller.

    • Main Results:

    • The proposed neuro-genetic controller demonstrated effective learning and adaptation capabilities.
    • Empirical simulations showed improved step response performance compared to a standalone PID controller.
    • Key performance metrics such as settling time, undershoot, and overshoot were significantly enhanced.

    Conclusions:

    • The hybrid neuro-genetic controller offers a viable and effective solution for controlling nonminimum phase systems.
    • This approach enhances control performance and adaptability beyond conventional methods.
    • The study validates the efficacy of combining genetic algorithms for neuro-controller training in complex systems.