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Related Experiment Video

Updated: Dec 31, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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An on-line modified least-mean-square algorithm for training neurofuzzy controllers.

Woei Wan Tan1

  • 1Department of Electrical and Computer Engineering, National University of Singapore, 4, Engineering Drive 3, 117576, Singapore. wwtan@nus.edu.sg

ISA Transactions
|March 6, 2007
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Summary
This summary is machine-generated.

This study introduces a novel fuzzy-based identification algorithm to improve online controller training by preventing learning interference. The method enhances data-driven modeling for processes with limited excitation, ensuring more robust controller performance.

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Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Process Systems Engineering

Background:

  • Online data-driven modeling for controller training is limited by insufficient plant excitation.
  • Constant setpoints can degrade process knowledge during online learning.
  • Learning interference is a key challenge in adaptive control systems.

Purpose of the Study:

  • To propose a new identification algorithm that mitigates learning interference in online controller training.
  • To enhance the robustness and speed of learning in data-driven control systems.
  • To evaluate the proposed algorithm's effectiveness in a practical liquid level control scenario.

Main Methods:

  • Incorporation of fuzzy theory into the normalized least-mean-square (NLMS) update rule.
  • Development of a novel identification algorithm to manage plant excitation.
  • Application of the algorithm to train a neurofuzzy feedforward controller for a liquid level process.

Main Results:

  • The proposed fuzzy-based identification algorithm effectively alleviates learning interference.
  • Faster learning rates were observed compared to traditional methods like NLMS and Recursive Least Squares (RLS).
  • Successful application in controlling a liquid level process demonstrated practical viability.

Conclusions:

  • The novel fuzzy identification strategy enhances online learning for data-driven controllers.
  • This approach offers a solution to the problem of insufficient plant excitation in adaptive control.
  • The method provides a more efficient and robust way to train controllers for dynamic processes.