Optimized Cascaded Position Control of BLDC Motors Considering Torque Ripple

TL;DR

This paper presents an optimized cascaded position control approach for BLDC motors, using multi-objective tuning and genetic algorithms to improve performance and reduce torque ripple.

Contribution

It introduces a novel multi-objective optimization method for cascaded control of BLDC motors considering torque ripple and position accuracy.

Findings

Optimized control parameters significantly reduce torque ripple.

Genetic algorithm improves disturbance rejection and response time.

Both control mechanisms benefit from the proposed optimization.

Abstract

Brushless DC (BLDC) motors are increasingly used in various industries due to their reliability, low noise, and extended lifespan compared to traditional DC motors. Their high torque-to-weight ratio and impressive starting torque make them ideal for automotive, robotics, and industrial applications. This paper explores the multi-objective tuning of BLDC motor controllers, focusing on position and torque ripple. A state-space model of the BLDC motor and the entire control system, including the power stage and control structure, is developed in the Simulink environment. Two common control mechanisms, trapezoidal and Field Oriented Control (FOC), are implemented and optimized. Both mechanisms utilize a cascaded closed-loop position control, providing fair disturbance rejection but requiring challenging tuning of the controllers. To address these challenges, the non-dominated sorting genetic algorithm II (NSGA-II) is used for optimization. This study demonstrates the effectiveness of optimization techniques in enhancing the performance of control systems.