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Fuzzy Logic and Genetic-Based Algorithm for a Servo Control System.

Hugo Torres-Salinas1, Juvenal Rodríguez-Reséndiz2, Edson E Cruz-Miguel3

  • 1Facultad de Informática, Universidad Autonóma de Querétaro, Querétaro 76230, Mexico.

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Summary
This summary is machine-generated.

This study introduces an optimized fuzzy logic position controller for 3D printer motion control. Genetic algorithms enhance performance, improving system response times and stability under varying conditions.

Keywords:
artificial intelligencefuzzy controllergenetic algorithmintelligent controloptimized controllerposition controller

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence Applications
  • Mechatronics Engineering

Background:

  • Conventional control methods struggle with time-varying parameters in regulated systems.
  • Artificial intelligence algorithms offer improved control solutions for dynamic environments.
  • Precise motion control is crucial for applications like 3D printing.

Purpose of the Study:

  • To implement and evaluate a novel position controller for a 3D printer's base using fuzzy logic.
  • To optimize the fuzzy logic controller's membership functions via genetic algorithms.
  • To assess the controller's performance under varied system trajectories and loads.

Main Methods:

  • Development of a real-time experimental platform including a 3D printer base, DC motor, power stage, and acquisition card.
  • Implementation of a fuzzy logic controller for position regulation.
  • Optimization of fuzzy logic membership functions using genetic algorithms.
  • Experimental validation across four distinct scenarios with modified trajectories and system weights.

Main Results:

  • The optimized fuzzy logic controller demonstrated superior performance compared to existing techniques, evidenced by reduced rising and setting times.
  • The controller maintained desired position accuracy despite changes in initial system conditions.
  • Robustness was confirmed under dynamic load and trajectory variations.

Conclusions:

  • Genetic algorithm-optimized fuzzy logic controllers offer enhanced precision and efficiency for motion control systems.
  • The proposed method provides a robust solution for real-time position regulation in dynamic environments.
  • This approach advances the application of AI in mechatronic systems like 3D printers.