PID算法图形 python

# -*- 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