scipy.signal.
ss2tf#
- scipy.signal.ss2tf(A, B, C, D, input=0)[source]#
- State-space to transfer function. - A, B, C, D defines a linear state-space system with p inputs, q outputs, and n state variables. - Parameters:
- Aarray_like
- State (or system) matrix of shape - (n, n)
- Barray_like
- Input matrix of shape - (n, p)
- Carray_like
- Output matrix of shape - (q, n)
- Darray_like
- Feedthrough (or feedforward) matrix of shape - (q, p)
- inputint, optional
- For multiple-input systems, the index of the input to use. 
 
- Returns:
- num2-D ndarray
- Numerator(s) of the resulting transfer function(s). num has one row for each of the system’s outputs. Each row is a sequence representation of the numerator polynomial. 
- den1-D ndarray
- Denominator of the resulting transfer function(s). den is a sequence representation of the denominator polynomial. 
 
 - Examples - Convert the state-space representation: \[ \begin{align}\begin{aligned}\begin{split}\dot{\textbf{x}}(t) = \begin{bmatrix} -2 & -1 \\ 1 & 0 \end{bmatrix} \textbf{x}(t) + \begin{bmatrix} 1 \\ 0 \end{bmatrix} \textbf{u}(t) \\\end{split}\\\textbf{y}(t) = \begin{bmatrix} 1 & 2 \end{bmatrix} \textbf{x}(t) + \begin{bmatrix} 1 \end{bmatrix} \textbf{u}(t)\end{aligned}\end{align} \]- >>> A = [[-2, -1], [1, 0]] >>> B = [[1], [0]] # 2-D column vector >>> C = [[1, 2]] # 2-D row vector >>> D = 1 - to the transfer function: \[H(s) = \frac{s^2 + 3s + 3}{s^2 + 2s + 1}\]- >>> from scipy.signal import ss2tf >>> ss2tf(A, B, C, D) (array([[1., 3., 3.]]), array([ 1., 2., 1.]))