Control Strategy for Brushless Motors

Brushless motors have been widely used in many fields due to their high efficiency, low noise, and long lifespan. To fully leverage the performance advantages of brushless motors, appropriate control strategies are crucial.

1、 Trapezoidal control (square wave control)

Trapezoidal control is a relatively simple brushless motor control method. Control the motor by generating a trapezoidal waveform of current. The advantage of this control strategy is that it is relatively easy to implement and does not require high hardware requirements. However, its efficiency is relatively low mainly because the current waveform is not in an ideal state and there are many harmonic components, resulting in significant energy loss. Trapezoidal control is suitable for application scenarios that do not require high performance, such as low-cost electric tools.

2、 Sine wave control

Sine wave control is a more advanced control strategy. It drives a brushless motor by generating a sinusoidal waveform of current. This control method can make the motor run more smoothly, with lower noise and higher efficiency. However, the algorithm and hardware requirements for sine wave control are relatively high, requiring more complex controllers and high-precision sensors to achieve precise control of the motor. Sine wave control is commonly used in high-end household appliances, precision instruments, and other equipment that require high performance.

3、 Magnetic field directional control (vector control)

Magnetic field oriented control decouples the magnetic field and torque of the motor, achieving precise speed and position control of the motor. This control strategy can enable brushless motors to maintain optimal performance under various operating conditions, with the highest efficiency and control accuracy. However, the algorithm and hardware for magnetic field orientation control are very complex, requiring powerful computing power and high-precision sensors. Therefore, it is mainly applied in fields that require extremely high control performance, such as industrial robots, CNC machine tools, etc.

4、 Direct torque control

Direct torque control directly controls the torque of the motor, achieving precise control of the motor. This control method has fast response speed, high efficiency, and can quickly respond to load changes. However, the algorithm for direct torque control is also quite complex and requires a deep understanding of the mathematical model of the motor. Direct torque control is applied in some situations that require high dynamic performance, such as the drive system of electric vehicles.

5、 Sliding mode control

Sliding mode control introduces a sliding surface into the dynamic equation of the motor, and achieves precise control by controlling the state of the motor to always move on the sliding surface. Sliding mode control has the advantages of strong robustness and anti-interference ability, and can maintain good control performance under changes in motor parameters and external disturbances. However, sliding mode control requires a deep understanding of the dynamic equations of the motor, and chattering phenomena may occur during the control process, requiring measures to be taken to suppress chattering.

6、 Adaptive control

Adaptive control can adjust control parameters online to adapt to changes in motor parameters and external disturbances. This control strategy can improve the robustness and performance of the system, enabling the motor to maintain good control performance under different working conditions. However, the algorithm of adaptive control is relatively complex, requiring a certain understanding of the mathematical model of the motor and a large amount of computational resources.

7、 Fuzzy control

Fuzzy control uses fuzzy logic to achieve precise control of motors. Fuzzy control can handle uncertainty and nonlinear problems, and has good control effects for some systems that are difficult to establish accurate mathematical models. However, fuzzy control requires the design of appropriate fuzzy rules and fuzzy inference algorithms, and continuous adjustment and optimization of control effects.

8、 Neural Network Control

Neural network control utilizes neural networks to achieve precise control of motors. Neural networks have strong learning abilities and the ability to handle complex nonlinear problems, and can adapt to different motor operating conditions and load changes. However, neural network control requires a large amount of training data and computing resources, and the training process is relatively complex, requiring professional knowledge and skills.

In short, there are various control strategies for brushless motors, each with its own advantages, disadvantages, and applicable scenarios. In practical applications, it is necessary to select appropriate control strategies based on specific needs and conditions to fully leverage the performance advantages of brushless motors.