ddcontrol

Control Theory for humans.


Keywords
data, driven, control, theorypid, tune, real, time, optimize
License
MIT
Install
pip install ddcontrol==0.6

Documentation

Documentation Status Build Status

Data-Driven Control

Control Theory for humans.
The PID controller design based entirely on experimental data collected from the plant.

Please Star me on GitHub for further development.

PID Controller Example

PIDController class can be used directly if the controller gains are already calculated. PIDController class is based on Thread Class. So it can be used as Tread. This features provides fixed PID loop frequency.

from ddcontrol.model import TransferFunction
from ddcontrol.control import PIDController
import numpy as np
import matplotlib.pyplot as plt
import time

#Creates PID controller and test model
pid = PIDController(kp=30, ki=70.0, kd=0.0, kn=0.0)
ref = 1.0
tf = TransferFunction([1.0], [1.0,10.0,20.0], udelay=0.1)

#Control loop
history = []
u = 0.0
pid.start()
start = time.time()
for _ in range(1000):
    t = time.time() - start
    y = tf.step(t, u)
    u = pid.update(ref-y)
    history.append([t,y])
    time.sleep(0.001)

#Stops PID controller
pid.stop()
pid.join()

#Plots result
np_hist = np.array(history)
fig, ax = plt.subplots()
ax.plot(np_hist[:,0], np_hist[:,1])
ax.grid()
plt.show()

Controlled output:

PID optimization for known Transfer Function

If the transfer function is already known, controller gains can be calculated by pidopt method.

from ddcontrol.model import TransferFunction
from ddcontrol.control import pidopt
import numpy as np
import matplotlib.pyplot as plt
import time

#Creates transfer function
tf = TransferFunction([1.0], [1.0,10.0,20.0], udelay=0.1)

#Predicts transfer function
pid, _ = pidopt(tf)
ref = 1.0

#Control loop
history = []
u = 0.0
pid.start()
start = time.time()
for _ in range(1000):
    t = time.time() - start
    y = tf.step(t, u)
    u = pid.update(ref-y)
    history.append([t,y])
    time.sleep(0.001)

#Stops PID controller
pid.stop()
pid.join()

#Plots result
np_hist = np.array(history)
fig, ax = plt.subplots()
ax.plot(np_hist[:,0], np_hist[:,1])
ax.grid()git 
plt.show()

Controlled output:

Transfer Function Estimation for unknown SISO system

If the transfer function is unknown for system, the transfer function can be estimated by tfest method.

from ddcontrol.model import TransferFunction, tfest
import numpy as np
import matplotlib.pyplot as plt

#Creates a transfer function and input output data
tf = TransferFunction([1.0], [1.0,10.0,20.0], 1.0)
t, y, u = np.linspace(0,10,101), np.zeros(101), np.ones(101)
for index in range(t.size):
    y[index] = tf.step(t[index], u[index])

#Predicts transfer function
tf_est, _ = tfest(t, y, u, np=2, nz=0, delay=True)
y_est = np.zeros(101)
for index in range(t.size):
    y_est[index] = tf_est.step(t[index], u[index])

#Plots result
fig, ax = plt.subplots()
ax.plot(t, y, '.-', label='Real')
ax.plot(t, y_est, '.-', label='Estimated')
ax.legend()
ax.grid()
plt.show()

Step response of real system and estimated system:

Installation

To install using pip:

pip install ddcontrol

Documentation

To read documentation:
https://ddcontrol.readthedocs.io/

Roadmap

  • tfest and pidopt functions are very slow. Performance improvement.
  • Real time PID controller tuning.