.Modelica_LinearSystems2.TransferFunction.Conversion.toMatrices

Information

Syntax

(A, B, C, D) = TransferFunction.Conversion.toStateSpacetoStateSpace(tf)

Description

Transforms a transfer function into state space representation. The outputs are the system functions A, B, C, D. There are an infinite number of possible realizations. Here, the transfer function is transformed into controller canonical form, i.e. the transfer function

     b4*s^4 + b3*s^3 + b2*s^2 + b1*s + b0
y = -------------------------------------- * u
     a4*s^4 + a3*s^3 + a2*s^2 + a1*s + a0

is transformed into:

der(x) = A*x + B*u;
    y  = C*x + D*u;
   with
           A = [   0  ,    1  ,    0  ,    0;
                   0  ,    0  ,    1  ,    0:
                   0  ,    0  ,    0  ,    1;
                -a0/a4, -a1/a4, -a2/a4, -a3/a4];
            B = [  0;
                  0;
                  0;
                 1/a4];
           C = [b0-b4*a0/a4, b1-b4*a1/a4, b2-b4*a2/a4, b3-b4*a3/a4];
           D = [b4/a4];

If the numerator polynomial is 1, then the state vector x is built up of y and of all derivatives of y up to nx-1 (nx is the dimension of the state vector):

x = {y, dy/dt, d^2y/dt^2, ..., d^(n-1)y/dt^(n-1)};

Note, the state vector x of Modelica.Blocks.Continuous.TransferFunction is defined slightly differently.

Example

  TransferFunction s = Modelica_LinearSystems2.TransferFunction.s();
  Modelica_LinearSystems2.TransferFunction tf=(s+1)/(s^3 + s^2 + s +1);

algorithm
  (A, B, C, D) := Modelica_LinearSystems2.TransferFunction.Conversion.toStateSpace(tf);
// A = [0, 1, 0; 0, 0, 1; -1, -1, -1],
// B = [0; 0; 1],
// C = [1, 1, 0],
// D = [0],

Interface

function toMatrices
  import Modelica;
  import Modelica_LinearSystems2;
  import Modelica_LinearSystems2.TransferFunction;
  import Modelica.Math.Vectors;
  input Modelica_LinearSystems2.TransferFunction tf "Transfer function of a system";
  output Real ABCD[size(tf.d, 1), size(tf.d, 1)];
end toMatrices;

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