FOC (Field Oriented Control) motor control algorithm is an advanced control technology for AC motors, designed to achieve precise control of motor speed and torque. The following are the basic principles and steps of the FOC motor control algorithm:
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Coordinate transformation: The three-phase current is converted into a two-axis current in the αβ coordinate system through Clarke transformation, where the α axis is in the same direction as the magnetic field and the β axis is perpendicular to the magnetic field. This converts the motor's three-phase system into two independent two-phase systems.
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Space vector PWM control: In the αβ coordinate system, the appropriate space vector PWM signal is generated according to the control requirements as the input voltage of the motor. These signals control motor speed and torque by adjusting amplitude, frequency and phase.
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Current closed-loop control: In FOC, the motor current is closed-loop controlled through the PID controller. By measuring and comparing the difference between the actual current and the expected current, the PWM signal is adjusted to achieve precise control of the current.
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Rotor position estimation: In order to achieve FOC control, the position of the motor rotor needs to be accurately estimated. Sensors (such as encoders) or sensorless methods (such as back electromotive force observers) are usually used to estimate the rotor position.
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Flux directional control: FOC controls the motor speed and torque by controlling the direction and size of the motor's magnetic field. Using the space vector PWM signal and the rotor position estimate, the motor's magnetic field can be accurately oriented to the desired angle.
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Speed closed-loop control: In FOC, closed-loop control of the speed can be achieved by measuring the motor speed and comparing it with the desired speed, and adjusting the phase of the magnetic field steering signal through the PID controller.
Through the above steps, the FOC motor control algorithm can achieve high-performance control of the AC motor and provide accurate speed and torque response. It is widely used in many fields such as industrial drives, electric vehicles, wind power generation and robots.
The following is a routine that uses the FOC algorithm to control a permanent magnet synchronous motor, using MATLAB/Simulink for modeling and simulation:
% FOC电机控制算法的Simulink模型
% 定义模型参数
R = 0.5; % 电机电阻
L = 0.01; % 电机电感
Psi = 0.1; % 磁链
J = 0.01; % 转动惯量
B = 0.1; % 阻尼系数
Ke = 1; % 反电动势系数
% 定义仿真时间
simulation_time = 1;
% 创建Simulink模型并配置
model = 'foc_motor_control';
open_system(new_system(model));
set_param(model, 'Solver', 'ode4', 'FixedStep', '0.001');
% 添加电机模型
add_block('simulink/Continuous/Integrator', [model '/Integrator1'], 'Position', [100, 100, 130, 130]);
add_block('simulink/Continuous/Integrator', [model '/Integrator2'], 'Position', [200, 100, 230, 130]);
add_block('simulink/Commonly Used Blocks/Sum', [model '/Sum1'], 'Position', [150, 100, 170, 120]);
add_block('simulink/Commonly Used Blocks/Sum', [model '/Sum2'], 'Position', [250, 100, 270, 120]);
add_block('simulink/Sources/Step', [model '/Step'], 'Position', [50, 100, 70, 120]);
add_block('simulink/Sinks/Scope', [model '/Scope'], 'Position', [350, 100, 370, 120]);
% 连接电机模型
add_line(model, 'Step/1', 'Sum1/1');
add_line(model, 'Sum1/1', 'Integrator1/1');
add_line(model, 'Integrator1/1', 'Integrator2/1');
add_line(model, 'Integrator1/1', 'Sum2/2');
add_line(model, 'Sum2/1', 'Integrator2/2');
add_line(model, 'Integrator2/1', 'Scope/1');
% 设定模型参数
set_param([model '/Integrator1'], 'InitialCondition', '0');
set_param([model '/Integrator2'], 'InitialCondition', '0');
set_param([model '/Step'], 'Time', num2str(simulation_time), 'SampleTime', '0.001');
set_param([model '/Scope'], 'TimeSpanOverrunBehavior', 'clip');
% 运行模型
sim(model);
% 绘制结果曲线
figure;
plot(Scope.time, Scope.signals.values);
xlabel('Time');
ylabel('Angular Position');
title('FOC Motor Control');
In the above routine, we first define the parameters of the motor (such as resistance, inductance, flux linkage, etc.). Then, a Simulink model was created and the motor model for FOC control was added.
The motor model uses two integrators to represent the angular position and angular velocity of the motor. By inputting the set value and PID controller to process the error, the control signal of the motor is obtained, and then the motor system is driven.
Finally, we ran the model through simulation and plotted the motor angular position over time.
It should be noted that the above routine only shows the basic ideas and examples of the FOC motor control algorithm. In actual applications, it needs to be appropriately adjusted and optimized according to specific motor parameters and system requirements. In addition, it can also be combined with the hardware platform to achieve actual control of the motor.