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Selection/control concurrent optimization of BLDC motors for industrial robots.

Erick Axel Padilla-García1, Héctor Cervantes-Culebro2, Alejandro Rodriguez-Angeles3

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This study introduces a model-based approach for selecting and controlling Brushless DC (BLDC) motors in industrial robots. The method optimizes energy consumption and tracking error, demonstrating significant improvements in performance and efficiency.

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Mechatronics

Background:

  • Industrial robots rely on efficient and precise actuators like Brushless DC (BLDC) motors.
  • Optimizing motor selection and control is crucial for enhancing robot performance, energy efficiency, and operational accuracy.
  • Existing methods may not concurrently address the multi-objective challenges of motor selection and dynamic control.

Purpose of the Study:

  • To develop and validate a synergistic model-based approach for the concurrent selection and control of BLDC motors in industrial robots.
  • To address the multi-objective dynamic optimization problem considering energy consumption, tracking error, and motor weight.
  • To improve the overall efficiency and precision of industrial robotic systems through optimized actuator selection and control.

Main Methods:

  • Modeling the three-phase dynamics of BLDC motors with trapezoidal back-EMF within a mechatronic powertrain model.
  • Defining the problem as a multi-objective dynamic optimization problem with static and dynamic constraints.
  • Employing a control parameterization approach with PI and PID controllers for voltage and torque, respectively, to solve the optimization problem.
  • Utilizing simulation and experimental validation to obtain a Pareto front and assess trade-offs.

Main Results:

  • Simulations yielded a Pareto front, illustrating the trade-offs between energy consumption, tracking error, and motor weight.
  • Experimental validation confirmed the effectiveness of the proposed synergistic approach.
  • The optimized selection and control resulted in up to 10.85% electrical power savings.
  • Trajectory tracking error was improved by as much as 57.41% compared to the original actuators.

Conclusions:

  • The synergistic model-based approach offers an effective solution for optimizing BLDC motor selection and control in industrial robots.
  • The methodology successfully balances competing objectives, leading to significant improvements in energy efficiency and motion precision.
  • This approach provides a valuable tool for enhancing the performance and reducing the operational costs of industrial robotic systems.