Now we can change our PID controller simulation using a real application. We use soil moisture to decide whether to pump water. The output of the measurement is used as feedback input for the PID controller.

If the PID output is a positive value, then we turn on the watering system. Otherwise, we stop it. This may not be a good approach but is a good way to show how PID controllers work. Soil moisture data is obtained from the Arduino through a wireless network.

Let's write this program:

import matplotlib 
matplotlib.use('Agg') 
 
import PID 
import time 
import matplotlib.pyplot as plt 
import numpy as np 
from scipy.interpolate import spline 
 
P = 1.4 
I = 1 
D = 0.001 
pid = PID.PID(P, I, D) 
 
pid.SetPoint = 0.0 
pid.setSampleTime(0.25)  # a second 
 
total_sampling = 100 
sampling_i = 0 
measurement = 0 
feedback = 0 
 
feedback_list = [] 
time_list = [] 
setpoint_list = [] 
 
 
def get_soil_moisture(): 
    # reading from Arduino 
    # value 0 - 1023 
    return 200 
 
 
 
print('PID controller is running..') 
try: 
    while 1: 
        pid.update(feedback) 
        output = pid.output 
 
        soil_moisture = get_soil_moisture()         
        if soil_moisture is not None: 
 
            # # ## testing 
            # if 23 < sampling_i < 50: 
            #     soil_moisture = 300 
             
            # if 65 <= sampling_i < 75: 
            #     soil_moisture = 350 
             
            # if sampling_i >= 85: 
            #     soil_moisture = 250 
            # # ################ 
                                 
            if pid.SetPoint > 0: 
                feedback += soil_moisture + output 
 
            print('i={0} desired.soil_moisture={1:0.1f} soil_moisture={2:0.1f} pid.out={3:0.1f} feedback={4:0.1f}' 
                  .format(sampling_i, pid.SetPoint, soil_moisture, output, feedback)) 
            if output > 0: 
                print('turn on watering system') 
            elif output < 0: 
                print('turn off watering system') 
 
        if 20 < sampling_i < 60: 
            pid.SetPoint = 300  # soil_moisture 
 
        if 60 <= sampling_i < 80: 
            pid.SetPoint = 200  # soil_moisture 
 
        if sampling_i >= 80: 
            pid.SetPoint = 260  # soil_moisture 
 
         
 
        time.sleep(0.5) 
        sampling_i += 1 
 
        feedback_list.append(feedback) 
        setpoint_list.append(pid.SetPoint) 
        time_list.append(sampling_i) 
 
        if sampling_i >= total_sampling: 
            break 
 
except KeyboardInterrupt: 
    print("exit") 
 
 
print("pid controller done.") 
print("generating a report...") 
time_sm = np.array(time_list) 
time_smooth = np.linspace(time_sm.min(), time_sm.max(), 300) 
feedback_smooth = spline(time_list, feedback_list, time_smooth) 
 
fig1 = plt.gcf() 
fig1.subplots_adjust(bottom=0.15, left=0.1) 
 
plt.plot(time_smooth, feedback_smooth, color='red') 
plt.plot(time_list, setpoint_list, color='blue') 
plt.xlim((0, total_sampling)) 
plt.ylim((min(feedback_list) - 0.5, max(feedback_list) + 0.5)) 
plt.xlabel('time (s)') 
plt.ylabel('PID (PV)') 
plt.title('Soil Moisture PID Controller') 
 
 
plt.grid(True) 
fig1.savefig('pid_soil_moisture.png', dpi=100) 
print("finish") 

Save this program to a file called ch01_pid.py and run it like this:

    $ sudo python ch01_pid.py  

After executing the program, you should obtain a file called pid_soil_moisture.png. A sample output can be seen in the following figure: