前馈补偿控制和pid控制代码
时间: 2023-08-24 13:10:05 浏览: 76
以下是一个简单的前馈补偿控制和PID控制的代码示例:
```python
import numpy as np
class FeedforwardPIDController:
def __init__(self, kp, ki, kd, feedforward_gain):
self.kp = kp
self.ki = ki
self.kd = kd
self.feedforward_gain = feedforward_gain
self.last_error = None
self.integral = 0
def control(self, setpoint, measured_value):
error = setpoint - measured_value
if self.last_error is None:
self.last_error = error
return self.feedforward_gain * setpoint
derivative = error - self.last_error
self.integral += error
output = self.feedforward_gain * setpoint + \
self.kp * error + \
self.ki * self.integral + \
self.kd * derivative
self.last_error = error
return output
if __name__ == "__main__":
# Example usage
controller = FeedforwardPIDController(kp=1.0, ki=0.1, kd=0.2, feedforward_gain=0.5)
# Simulate a process with a step change in setpoint
setpoints = np.concatenate([
np.ones(25) * 10,
np.ones(25) * 20,
np.ones(25) * 30,
np.ones(25) * 40,
])
measured_values = np.zeros_like(setpoints)
for i, setpoint in enumerate(setpoints):
measured_value = measured_values[i-1] if i > 0 else 0
output = controller.control(setpoint, measured_value)
measured_value = measured_value + (output - measured_value) * 0.1 # Simulate process
measured_values[i] = measured_value
# Plot results
import matplotlib.pyplot as plt
plt.plot(setpoints, label="setpoint")
plt.plot(measured_values, label="measured value")
plt.legend()
plt.show()
```
在这个示例中,我们定义了一个名为FeedforwardPIDController的类,它具有前馈增益和PID增益。`control`方法接受一个设定值和一个测量值,并计算输出值。我们还定义了一个简单的模拟过程来测试控制器,该过程模拟了一个具有阶跃变化的设定点的过程。最后,我们使用matplotlib库绘制了控制器的输出和设定点之间的关系。
注意:这只是一个示例,实际上,前馈补偿控制和PID控制的实现可能会因应用场景而各有不同。