PID算法图形 python

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# -*- coding: utf-8 -*-     class PID:     def __init__(self, P=0.2, I=0.0, D=0.0):         self.Kp = P         self.Ki = I         self.Kd = D         self.sample_time = 0.00         self.current_time = time.time()         self.last_time = self.current_time         self.clear()       def clear(self):         self.SetPoint = 0.0         self.PTerm = 0.0         self.ITerm = 0.0         self.DTerm = 0.0         self.last_error = 0.0         self.int_error = 0.0         self.windup_guard = 20.0         self.output = 0.0       def update(self, feedback_value):         error = self.SetPoint - feedback_value         self.current_time = time.time()         delta_time = self.current_time - self.last_time         delta_error = error - self.last_error         if (delta_time >= self.sample_time):             self.PTerm = self.Kp * error  # 比例             self.ITerm += error * delta_time  # 积分             if (self.ITerm < -self.windup_guard):                 self.ITerm = -self.windup_guard             elif (self.ITerm > self.windup_guard):                 self.ITerm = self.windup_guard             self.DTerm = 0.0             if delta_time > 0:                 self.DTerm = delta_error / delta_time             self.last_time = self.current_time             self.last_error = error             self.output = self.PTerm + (self.Ki * self.ITerm) + (self.Kd * self.DTerm)       def setKp(self, proportional_gain):         self.Kp = proportional_gain       def setKi(self, integral_gain):         self.Ki = integral_gain       def setKd(self, derivative_gain):         self.Kd = derivative_gain       def setWindup(self, windup):         self.windup_guard = windup       def setSampleTime(self, sample_time):         self.sample_time = sample_time     import time import matplotlib   matplotlib.use("TkAgg") import matplotlib.pyplot as plt import numpy as np from scipy.interpolate import make_interp_spline         def test_pid(P=0.2, I=0.0, D=0.0, L=100):           pid = PID(P, I, D)       pid.SetPoint = 0.0     pid.setSampleTime(0.01)       END = L     feedback = 0       feedback_list = []     time_list = []     setpoint_list = []       for i in range(1, END):         pid.update(feedback)         output = pid.output         # print(output)         if pid.SetPoint > 0:             feedback += output  # (output - (1/i))控制系统的函数         if 9 <= i <= 40:             pid.SetPoint = 1         elif i > 40:             pid.SetPoint = 0.5           time.sleep(0.01)           feedback_list.append(feedback)         setpoint_list.append(pid.SetPoint)         time_list.append(i)       time_sm = np.array(time_list)     time_smooth = np.linspace(time_sm.min(), time_sm.max(), 300)     feedback_smooth = make_interp_spline(time_list, feedback_list)(time_smooth)     plt.figure(0)     plt.plot(time_smooth, feedback_smooth)     plt.plot(time_list, setpoint_list)       plt.xlabel('time (s)')     plt.ylabel('PID (PV)')     plt.title('TEST PID {}/{}/{}'.format(P, I, D))       plt.xlim((0, L))     plt.ylim((-0.5, 2))       plt.grid(True)     # plt.show()     plt.savefig('./images/TEST PID {}-{}-{}.jpg'.format(P, I, D))     plt.close()     if __name__ == "__main__":     test_pid(1.2, 1.0, 0.001, L=50)     test_pid(1.2, 1.0, 0, L=50)     test_pid(1.2, 0, 0, L=50)       test_pid(0.8, 1.0, 0.001, L=50)     test_pid(0.8, 1.0, 0, L=50)     test_pid(0.8, 0, 0, L=50)       test_pid(0.2, 0.0, 0.001, L=50)     test_pid(0.2, 0.0, 0, L=50)     test_pid(0.2, 0, 0, L=50)       test_pid(0.8, 0, 0.001, L=50)     test_pid(1.2, 0, 0.001, L=50)       test_pid(0.7, 0.8, 0.001, L=50)       test_pid(0.8, L=50)

　　

 

模拟了电动机电压的输出：

• 从0秒开始到第9秒，要求输出电压为0V；
• 从第10秒开始到第40秒，要求输出电压为1V；
• 从第41秒开始到第50秒，要求输出电压为0.5V

橘黄色线代表上述需求（理想输出电压）

绿色线为PID算法输出带反馈积分的输出电压

看得到P（比例）部分 是一个最重要的参数、I（积分）部分能让两条线完全重合（可能过于理想，有待验证）、D（微分）部分会对电压产生微调的上下波动影响

PID算法的参数看来是能够影响元器件寿命的