Scilab Function

lqg2stan - LQG to standard problem

### Calling Sequence

  `[P,r]=lqg2stan(P22,bigQ,bigR)`

### Parameters

- `P22` : `syslin` list (nominal plant) in state-space form
- `bigQ` : `[Q,S;S',N]` (symmetric) weighting matrix
- `bigR` : `[R,T;T',V]` (symmetric) covariance matrix
- `r` : `1` x`2` row vector = (number of measurements, number of inputs) (dimension of the 2,2 part of `P` )
- `P` : `syslin` list (augmented plant)

### Description

`lqg2stan` returns the augmented plant for linear LQG (H2) controller design.

`P22=syslin(dom,A,B2,C2)` is the nominal plant; it can be in continuous time (`dom='c'` ) or discrete time (`dom='d'` ).

      .
      x = Ax + w1 + B2u
      y = C2x + w2

for continuous time plant.

      x[n+1]= Ax[n] + w1 + B2u
          y = C2x + w2

for discrete time plant.

The (instantaneous) cost function is `[x' u'] bigQ [x;u]` .

The covariance of `[w1;w2]` is `E[w1;w2] [w1',w2'] = bigR`

If `[B1;D21]` is a factor of `bigQ` , `[C1,D12]` is a factor of `bigR` and `[A,B2,C2,D22]` is a realization of P22, then `P` is a realization of `[A,[B1,B2],[C1,-C2],[0,D12;D21,D22]` . The (negative) feedback computed by `lqg` stabilizes `P22` , i.e. the poles of `cl=P22/.K` are stable.

### Examples

    ny=2;nu=3;nx=4;
    P22=ssrand(ny,nu,nx);
    bigQ=rand(nx+nu,nx+nu);bigQ=bigQ*bigQ';
    bigR=rand(nx+ny,nx+ny);bigR=bigR*bigR';
    [P,r]=lqg2stan(P22,bigQ,bigR);K=lqg(P,r);  //K=LQG-controller
    spec(h_cl(P,r,K))      //Closed loop should be stable
    //Same as Cl=P22/.K; spec(Cl('A'))
    s=poly(0,'s')
    lqg2stan(1/(s+2),eye(2,2),eye(2,2))

### See Also

`lqg` , `lqr` , `lqe` , `obscont` , `h_inf` , `augment` , `fstabst` , `feedback` ,

### Author

F.D.