liba 0.1.15

An algorithm library based on C/C++
Documentation 
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#!/usr/bin/env python
import os, sys

base = os.path.dirname(__file__)
path = os.path.dirname(base)
path = os.path.dirname(path)
sys.path.insert(0, path)
try:
    import numpy as np
    import matplotlib.pyplot as plt
except Exception as e:
    print(e)
    exit()

Ts = 0.001
try:
    import control.matlab as ct

    sysc = ct.tf(133, [1, 25, 0])
    sysd = ct.c2d(sysc, Ts)
    print(sysc, sysd)

    [[num]], [[den]] = ct.tfdata(sysd)
except ModuleNotFoundError:
    num = [6.59492796e-05, 6.54019884e-05]
    den = [1.0, -1.97530991, 0.97530991]
except Exception as e:
    print(e)
    exit()
import liba  # type: ignore

MIN = -10
MAX = +10

kp = 10.0
ki = 0.01 * Ts
kd = 0.24 / Ts

tf = liba.tf(num, den[1:])
pid = liba.pid().set_kpid(kp, ki, kd)
pid.outmax = MAX
pid.outmin = MIN

r = 1.0
data = np.arange(0, 0.2, Ts)
setpoint = [r] * len(data)

title = "Proportional Integral Derivative Position"

y = 0.0
tf.zero()
error1 = []
feedback1 = []
pid.zero()
for i in data:
    u = pid.pos(r, y)
    y = tf(u)
    feedback1.append(y)
    error1.append(r - y)

y = 0.0
tf.zero()
error2 = []
feedback2 = []
e = [0.0, 0.0, 0.0]
x = [0.0, 0.0, 0.0]
for i in data:
    e = np.roll(e, 1)
    e[0] = r - y
    x[0] = e[0]
    x[1] += ki * e[0]
    x[2] = e[0] - e[1]
    u = kp * x[0] + x[1] + kd * x[2]
    if u < MIN:
        u = MIN
    elif u > MAX:
        u = MAX
    y = tf(u)
    feedback2.append(y)
    error2.append(r - y)

plt.figure(title)
plt.subplot(211)
plt.title(title)
plt.plot(data, setpoint, "r-", data, feedback1, "b-", data, feedback2, "g-")
plt.ylabel("r - y")
plt.grid(True)
plt.subplot(212)
plt.plot(data, error1, "b-", data, error2, "g-")
plt.ylabel("error")
plt.xlabel("time(s)")
plt.grid(True)
plt.savefig(os.path.join(base, "pid_pos.png"))

title = "Proportional Integral Derivative Increment"

y = 0.0
tf.zero()
error1 = []
feedback1 = []
pid.zero()
for i in data:
    u = pid.inc(r, y)
    y = tf(u)
    feedback1.append(y)
    error1.append(r - y)

u = 0.0
y = 0.0
tf.zero()
error2 = []
feedback2 = []
e = [0.0, 0.0, 0.0]
x = [0.0, 0.0, 0.0]
for i in data:
    e = np.roll(e, 1)
    e[0] = r - y
    x[0] = e[0] - e[1]
    x[1] = e[0]
    x[2] = e[0] - e[1] * 2 + e[2]
    u += kp * x[0] + ki * x[1] + kd * x[2]
    y = u
    if y < MIN:
        y = MIN
    elif y > MAX:
        y = MAX
    y = tf(y)
    feedback2.append(y)
    error2.append(r - y)

plt.figure(title)
plt.subplot(211)
plt.title(title)
plt.plot(data, setpoint, "r-", data, feedback1, "b-", data, feedback2, "g-")
plt.ylabel("r - y")
plt.grid(True)
plt.subplot(212)
plt.plot(data, error1, "b-", data, error2, "g-")
plt.ylabel("error")
plt.xlabel("time(s)")
plt.grid(True)
plt.savefig(os.path.join(base, "pid_inc.png"))

plt.show()