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Robust controller design and experimental validation based on bounded uncertainty for collaborative industrial

Hu Cheng1,2, Hongmei Zheng1, Shengchao Zhen1,2

  • 1School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China.

The Review of Scientific Instruments
|August 2, 2024
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Summary
This summary is machine-generated.

A new robust control method for six-axis cooperative industrial robots enhances stability and tracking performance by addressing friction and uncertainty. This dynamic feedforward approach improves robustness compared to traditional methods.

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Mechatronics

Background:

  • Industrial robots, particularly six-axis cooperative systems, face challenges from nonlinear friction, parameter uncertainty, and external disturbances.
  • Existing control methods like proportional-integral-derivative (PID) and mode-based proportional-derivative (PD) control may not sufficiently address these complex dynamics for optimal performance.
  • Robust control strategies are essential for ensuring reliable and precise operation in dynamic industrial environments.

Purpose of the Study:

  • To develop a novel, practical robust control method for six-axis motion cooperative industrial robots.
  • To incorporate a dynamic feedforward model alongside proportional-derivative (PD) and robust control principles.
  • To systematically account for nonlinear friction, parameter uncertainty, and external disturbances within the robot's dynamic model.

Main Methods:

  • A dynamic feedforward model was established for six-axis cooperative industrial robots, integrating nonlinear friction, parameter uncertainty, and external disturbance considerations.
  • The control strategy combines proportional-derivative (PD) control with robust control principles.
  • Lyapunov theory was employed to rigorously analyze the controller's stability, proving uniformly bounded and uniformly final bounded system properties.

Main Results:

  • The proposed robust control method demonstrated superior stability tracking performance and robustness compared to conventional proportional-integral-derivative (PID) and mode-based proportional-derivative (PD) control.
  • Simulation and experimental results validated the effectiveness of the novel controller in challenging dynamic conditions.
  • The use of the CSPACE platform facilitated rapid controller prototyping, significantly reducing programming effort and simplifying experimental trials.

Conclusions:

  • The developed practical robust control method offers enhanced performance for six-axis cooperative industrial robots, outperforming existing techniques.
  • The controller effectively guarantees system stability and robustness, making it suitable for complex industrial applications.
  • The integration of dynamic feedforward modeling and advanced control principles, coupled with efficient prototyping platforms, represents a significant advancement in industrial robot control.