Pyctrl

Pyctrl is an open source modern control library

Features

• state space to transfer function
• tranfer function to state space
• System Solution
• Controlability
• Observability
• Stabillity
• Stepresonse
• Pole placement

Dependencies

• numpy
• sympy
• mpmath
• matplotlib

Documentation

importing:

from pyctrl import *
pyc=pyctrl()

convertion from state space to transfer function¶

In 

A=np.array([[3, 10],[5,2]])
B=np.array([1,1])
C=np.array([1,0])
D=np.array([0])

pyc.ss2tf(A,B,C,D)

Out:

conversion from transfer function to state space¶

the default is the controllble form

In :

n=np.array([4])
d=np.array([1,2,10])

pyc.tf2ss(n,d)

Out:

(array([[  0.,   1.],
        [-10.,  -2.]]),
 array([0., 1.]),
 array([0., 4.]),
 array([0.]))

In :

pyc.tf2ss(n,d,"observable")

Out:

(array([[  0., -10.],
        [  1.,  -2.]]),
 array([0., 4.]),
 array([0., 1.]),
 array([0.]))

solution of a system¶

In :

A=np.array([[3, 10],[5,2]])
B=np.array([1,1])
x0=np.array([1,0])
u=1

pyc.solve(A,B,x0,u)

Out:

checking for stability, observability, and controllability¶

In [12]:

A=np.array([[3, 10],[5,2]])
B=np.array([1,1])
C=np.array([1,0])

stability

In [13]:

pyc.isStable(A)

Out[13]:

True

observability

In [14]:

pyc.isObservable(A,C)

Out[14]:

True

controllability

In [15]:

pyc.isControllable(A,B)

Out:

True

step response of the system¶

In :

pyc.step(n,d)

Out:

Pole placplacement¶

In :

A=np.array([[0, 1,0],[0,0,1],[-2,-3,-5]])
B=np.array([0,0,1])

pyc.placePoles(A,B,-4+3j,-4-3j,-4)

Out[8]:

Contributing

Contributions are always welcome!