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

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.