How it works

First, we define our PID parameters:

P = 1.4 
I = 1 
D = 0.001 
pid = PID.PID(P, I, D) 
 
pid.SetPoint = 0.0 
pid.setSampleTime(0.01) 
 
total_sampling = 100 
feedback = 0 
 
feedback_list = [] 
time_list = [] 
setpoint_list = [] 

After that, we compute the PID value during sampling. In this case, we set the desired output values as follows:

• Output 1 for sampling from 20 to 60
• Output 0.5 for sampling from 60 to 80
• Output 1.3 for sampling more than 80

for i in range(1, total_sampling): 
    pid.update(feedback) 
    output = pid.output 
    if pid.SetPoint > 0: 
        feedback += (output - (1 / i)) 
 
    if 20 < i < 60: 
        pid.SetPoint = 1 
 
    if 60 <= i < 80: 
        pid.SetPoint = 0.5 
 
    if i >= 80: 
        pid.SetPoint = 1.3 
 
    time.sleep(0.02) 
 
    feedback_list.append(feedback) 
    setpoint_list.append(pid.SetPoint) 
    time_list.append(i) 

The last step is to generate a report and save it into a file called result.png:

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) 
 
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('TEST PID') 
 
 
plt.grid(True) 
print("saving...") 
fig1.savefig('result.png', dpi=100)