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Embedded intelligent adaptive PI controller for an electromechanical system.

Ahmad M El-Nagar1

  • 1Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menofia University, Menuf 32852, Egypt.

ISA Transactions
|June 26, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive controller using an interval type-2 fuzzy neural network (IT2FNN) to tune a proportional-integral (PI) controller. The novel approach enhances DC motor speed control, improving system response under uncertainties.

Keywords:
Adaptive PI controllerInterval type-2 fuzzy neural networksLyapunov theoremNonlinear DC motor

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Electrical Engineering

Background:

  • DC motor-generator systems exhibit nonlinear dynamics, posing challenges for precise speed control.
  • Traditional controllers often struggle with system uncertainties and parameter variations.
  • Intelligent adaptive control offers a promising avenue for robust system performance.

Purpose of the Study:

  • To develop and practically implement an intelligent adaptive controller for nonlinear DC motor-generator speed control.
  • To integrate an interval type-2 fuzzy neural network (IT2FNN) for online tuning of a proportional-integral (PI) controller.
  • To evaluate the performance of the proposed adaptive PI controller based on IT2FNN (API-IT2FNN) against other control methods.

Main Methods:

  • An adaptive PI controller (API-IT2FNN) was designed, featuring a lower-level PI controller and an upper-level IT2FNN for parameter tuning.
  • The IT2FNN parameters were tuned online using the back-propagation algorithm.
  • The controller was implemented on an Arduino DUE kit for experimental validation on a nonlinear DC motor-generator system.
  • Stability and convergence of the IT2FNN were mathematically derived using Lyapunov theorem.

Main Results:

  • The API-IT2FNN demonstrated superior performance in controlling the speed of the nonlinear DC motor-generator system.
  • Experimental results showed significant improvement in system response compared to other controllers.
  • The controller effectively handled a wide range of system uncertainties, showcasing robustness.

Conclusions:

  • The proposed API-IT2FNN provides an effective intelligent adaptive control strategy for nonlinear DC motor speed control.
  • The IT2FNN's online tuning capability enhances controller adaptability and robustness.
  • Practical implementation confirmed the controller's ability to improve system response and handle uncertainties.