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Related Experiment Videos

Calculation of PID controller parameters by using a fuzzy neural network.

Ching-Hung Lee1, Ching-Cheng Teng

  • 1Department of Electrical Engineering, Yuan Ze University 135 Yuan Tung Rd, Chung-Li, Taoyuan 320, Taiwan, Republic of China. chlee@saturn.yzu.edu.tw

ISA Transactions
|July 16, 2003
PubMed
Summary

This study introduces a fuzzy neural network (FNN) to automatically design proportional-integral-derivative (PID) controllers. The new method optimizes for minimum integrated absolute error (IAE) and maximum sensitivity (Ms), simplifying controller tuning.

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence in Engineering
  • Automation and Robotics

Background:

  • Traditional PID controller tuning often relies on theoretical or numerical methods, which can be complex and time-consuming.
  • Achieving optimal performance requires balancing competing criteria like minimizing error and maintaining system stability (sensitivity).

Purpose of the Study:

  • To develop an automated formula for designing Proportional-Integral-Derivative (PID) controllers using a Fuzzy Neural Network (FNN).
  • To ensure the designed PID controller meets specific performance criteria: minimum Integrated Absolute Error (IAE) and maximum Sensitivity (Ms).

Main Methods:

  • Utilized a Fuzzy Neural Network (FNN) to model the relationship between plant parameters and PID controller gains.
  • Applied the dominant pole assignment method to simplify the optimization process for tuning rules.

Related Experiment Videos

  • Developed an FNN-based formula for automatic PID controller tuning, eliminating the need for manual theoretical or numerical approaches.

    • Main Results:

    • The FNN system successfully identified the plant model and controller parameter relationships based on IAE and Ms.
    • The developed FNN-based formula enables automatic tuning of PID controllers for varying system parameters.
    • The approach demonstrated effectiveness in a motor position control simulation, showing adaptability to system model changes.

    Conclusions:

    • The proposed FNN-based approach provides an effective and automated method for designing PID controllers.
    • This technique simplifies the tuning process while optimizing for key performance metrics like IAE and Ms.
    • The FNN-based formula offers a robust solution for adaptive controller modification in dynamic systems.