Scilab Function

gfrancis - Francis equations for tracking

### Calling Sequence

  `[L,M,T]=gfrancis(Plant,Model)`

### Parameters

- `Plant` : `syslin` list
- `Model` : `syslin` list
- `L,M,T` : real matrices

### Description

Given the the linear plant:

        x'= F*x + G*u
        y = H*x + J*u

and the linear model

        xm'= A*xm + B*um
        ym = C*xm + D*um

the goal is for the plant to track the model i.e. `e = y - ym ---> 0` while keeping stable the state x(t) of the plant. `u` is given by feedforward and feedback

     u = L*xm + M*um + K*(x-T*xm) = [K , L-K*T] *(x,xm) + M*um

The matrices T,L,M satisfy generalized Francis equations

        F*T + G*L = T*A
        H*T + J*L = C
              G*M = T*B
              J*M = D

The matrix `K` must be chosen as stabilizing the pair `(F,G)` See example of use in directory `demos/tracking`.

### Examples

    Plant=ssrand(1,3,5);
    [F,G,H,J]=abcd(Plant);
    nw=4;nuu=2;A=rand(nw,nw);
    st=maxi(real(spec(A)));A=A-st*eye(A);
    B=rand(nw,nuu);C=2*rand(1,nw);D=0*rand(C*B);
    Model=syslin('c',A,B,C,D);
    [L,M,T]=gfrancis(Plant,Model);
    norm(F*T+G*L-T*A,1)
    norm(H*T+J*L-C,1)
    norm(G*M-T*B,1)
    norm(J*M-D,1)

### See Also

`lqg` `ppol`