Currently, multiplying a SISO TransferFunction object by a NumPy array results in a dimension error:

import control
import numpy as np

tf = control.TransferFunction([1], [1, 0])
arr = np.array([
    [1, 0],
    [2, 1],
])

print(tf * arr)
print(arr * tf)

ValueError: C = A * B: A has 1 column(s) (input(s)), but B has 2 row(s) (output(s)).

I think it would be convenient if output was a MIMO TransferFunction object of the array's dimension. So, for example,

G = control.TransferFunction(
    [
        [[1], [0]],
        [[2], [1]],
    ],
    [
        [[1, 0], [1, 0]],
        [[1, 0], [1, 0]],
    ],
)

I would be happy to implement this and do a PR, but I want to make sure it's something the maintainers actually want. I would think all SISO systems (state-space, etc) should behave in the same way.

This appears to have been discussed in #459 , but I'm not sure what the conclusion was.