zero dynamics within MIMO transfer functions leads to errors
ilayn opened this issue · 2 comments
If a transfer matrix involves scalars, e.g.,
[ | 1 ]
G = [ 1 | ----- ]
[ | s+1 ]
after the separation of the feed-through, still treated as a polynomial entry and looks for least common denominators.
Extra check is necessary for empty dynamics in entries.
Moreover, tf(5), ss(5) still causes problems.
Needs a proper gateway to be written to account for these.
Offending input was
G = tf([[[1,1],[1,1]],[[1],[1,2,1]]],[[[1,2],[1,5,6]],[[1],[1,7,10]]])
However, state representation has no problems
A = blkdiag([-2],[[-5,-3],[2,0]],[-5])
B = np.array([[1,0,0,0],[0,1,0,1]]).T
C = np.array([[-1,1,0.5,0],[0,0,0,1]])
D = np.array([[1,0],[1,0]])
G2 = ss(A,B,C,D)
It leads to
Continous-time state represantation
2 input(s) and 2 output(s)
Poles(real) Poles(imag) Zeros(real) Zeros(imag)
------------- ------------- ------------- -------------
-2 0 -1 -0
-3 0 -2 -0
-2 0
-5 0
But conversion to transfer, gives nonsense zeros due to wrong initialization
G2tf = tf(*ss2tf(G2))
Numpy's polynomial module has this strange coefficient reversal behavior and I was using it before I gave up and wrapped harold's own functions. Thus there were a lot of array reversal points involving a[::-1] type of coding.
It seems that while I was removing the numpy.polynomial.polynomial dependence, I have removed one too many and the C matrix coefficients were entering in the reversed order. Now, after some testing, looks like it is fixed.