block StateSpace "Discrete State Space block"
parameter Real A[:,size(A, 1)] = [1,0; 0,1] "Matrix A of state space model";
parameter Real B[size(A, 1),:] = [1; 1] "Matrix B of state space model";
parameter Real C[:,size(A, 1)] = [1,1] "Matrix C of state space model";
parameter Real D[size(C, 1),size(B, 2)] = zeros(size(C, 1), size(B, 2)) "Matrix D of state space model";
extends Interfaces.DiscreteMIMO(final nin = size(B, 2), final nout = size(C, 1));
output Real x[size(A, 1)] "State vector";
equation
when sampleTrigger then
x = A * pre(x) + B * u;
y = C * pre(x) + D * u;
end when;
annotation (
Documentation(info = "<html>\n<p>\nThe <strong>discrete state space</strong> block defines the relation\nbetween the input u and the output y in state space form:\n</p>\n<pre>\n x = A * pre(x) + B * u\n y = C * pre(x) + D * u\n</pre>\n<p>\nwhere pre(x) is the value of the discrete state x at\nthe previous sample time instant.\nThe input is a vector of length nu, the output is a vector\nof length ny and nx is the number of states. Accordingly\n</p>\n<pre>\n A has the dimension: A(nx,nx),\n B has the dimension: B(nx,nu),\n C has the dimension: C(ny,nx),\n D has the dimension: D(ny,nu)\n</pre>\n<p>\nExample:\n</p>\n<pre>\n parameter: A = [0.12, 2;3, 1.5]\n parameter: B = [2, 7;3, 1]\n parameter: C = [0.1, 2]\n parameter: D = zeros(ny,nu)\n\nresults in the following equations:\n [x[1]] [0.12 2.00] [pre(x[1])] [2.0 7.0] [u[1]]\n [ ] = [ ]*[ ] + [ ]*[ ]\n [x[2]] [3.00 1.50] [pre(x[2])] [0.1 2.0] [u[2]]\n [pre(x[1])] [u[1]]\n y[1] = [0.1 2.0] * [ ] + [0 0] * [ ]\n [pre(x[2])] [u[2]]\n</pre>\n</html>"),
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end StateSpace;