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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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A New Methodology for a Retrofitted Self-tuned Controller with Open-Source FPGA.

Edson E Cruz-Miguel1, José R García-Martínez1, Juvenal Rodríguez-Reséndiz1

  • 1División de Investigación y Posgrado, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico.

Sensors (Basel, Switzerland)
|November 3, 2020
PubMed
Summary

This study introduces an open-source servo controller using reconfigurable hardware and a Genetic Algorithm for online self-tuning. This approach enhances servo control quality and reduces vibrations in linear motion systems.

Keywords:
adaptive and predictive controlcontroller self-tuninggenetic algorithminstrumentation and sensorsopen-hardware FPGAretrofittedvibration analysis

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Area of Science:

  • Control Engineering
  • Mechatronics
  • Open Source Hardware

Background:

  • Electronic obsolescence in industrial servo systems is a persistent challenge.
  • Conventional servo tuning methods lack adaptability and optimization capabilities.
  • Need for flexible, updatable, and cost-effective servo control solutions.

Purpose of the Study:

  • Develop an open-architecture servo controller using reconfigurable hardware and open-source principles.
  • Implement a Genetic Algorithm for online self-tuning to optimize control performance and reduce vibrations.
  • Validate the developed controller on a real platform and compare its performance against manufacturer software.

Main Methods:

  • Design and implementation of an open-architecture controller on reconfigurable hardware (FPGA).
  • Integration of a Genetic Algorithm for real-time, online self-tuning of servo system parameters.
  • Development of a Graphical User Interface (GUI) in Python for system interaction and monitoring.
  • Validation experiments on a linear motion system to assess control accuracy and vibration reduction.

Main Results:

  • Successful implementation of an open-source servo controller with online self-tuning capabilities.
  • Demonstrated reduction in vibrations during linear motion system positioning.
  • Validated effectiveness of the Genetic Algorithm for adaptive parameter optimization.
  • Python-based GUI provides features like speed profiling, auto-tuning, and vibration monitoring.

Conclusions:

  • The proposed open-source controller methodology offers a novel and effective approach to servo control.
  • Online self-tuning via Genetic Algorithms improves control quality and mitigates vibrations.
  • Utilizing free tools for FPGA implementation is viable and competitive with manufacturer software.
  • The approach combats electronic obsolescence and facilitates easy system modification and updates.