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Probabilistic fuzzy neural network-based indirect adaptive control framework for dynamic systems.

A Aziz Khater1, Eslam M Gaballah1, Mohammad El-Bardini1

  • 1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Egypt.

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|July 17, 2025
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Summary
This summary is machine-generated.

This study introduces a probabilistic Takagi-Sugeno-Kang fuzzy neural network (PTSK-FNN) for adaptive control. The novel approach enhances PID controller performance by managing uncertainties and ensuring stability, outperforming existing methods.

Keywords:
And Structure LearningIndirect Adaptive ControlLyapunov CriteriaProbabilistic Control TheoryProbabilistic Fuzzy Neural NetworkWeiner-Model

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Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Fuzzy Systems

Background:

  • Adaptive control systems require robust methods to handle system uncertainties and disturbances.
  • Proportional-Integral-Derivative (PID) controllers are widely used but can struggle with complex nonlinear dynamics.
  • Fuzzy neural networks offer a powerful framework for modeling and control, but integrating probabilistic processing enhances their capabilities.

Purpose of the Study:

  • To introduce a probabilistic Takagi-Sugeno-Kang fuzzy neural network (PTSK-FNN) for indirect adaptive control.
  • To develop a novel online learning algorithm based on Lyapunov theorem for guaranteed system stability.
  • To improve system identification for accurate control signal calculation and enhance PID controller performance.

Main Methods:

  • Utilizing a Wiener model with PTSK-FNN for system identification of linear and nonlinear dynamics.
  • Dynamically modifying PTSK-FNN structure and parameters to update PID controller gains.
  • Implementing a probabilistic approach within the TSK fuzzy neural system to manage chaotic uncertainties.

Main Results:

  • The proposed PTSK-FNN based adaptive controller significantly outperforms existing controllers in mitigating noise, disturbances, and uncertainties.
  • Achieved a reduction in mean absolute error by 34.2% in simulations and 38.6% in experimental results.
  • Demonstrated superior performance in nonlinear dynamic systems through simulations and experimental validation.

Conclusions:

  • The probabilistic Takagi-Sugeno-Kang fuzzy neural network provides a reliable framework for indirect adaptive control.
  • The developed adaptive control strategy effectively enhances PID controller performance for nonlinear systems.
  • This approach offers a robust solution for engineering applications requiring precise control under uncertain conditions.