lqe

Arguments

Pw

A state space representation of a linear dynamical system (see syslin)

P

A state space representation of a linear dynamical system (nu inputs, ny outputs, nx states) (see syslin)

Qww

Real nx by nx symmetric matrix, the process noise variance

Rvv

full rank real ny by ny symmetric matrix, the measurement noise variance.

Swv

real nx by ny matrix, the process noise vs measurement noise covariance. The default value is zeros(nx,ny).

K

a real matrix, the optimal gain.

X

a real symmetric matrix, the stabilizing solution of the Riccati equation.

Description

This function computes the linear optimal LQ estimator gain of the state estimator for a detectable (see dt_ility) linear dynamical system and the variance matrices for the process and the measurement noises.

Syntax [K,X]=lqe(P,Qww,Rvv [,Swv])

Computes the linear optimal LQ estimator gain K for the dynamical system:

or

Where w and v are white noises such as

The values of B and D are not taken into account.

Standard form

This covariance matrix can be factored using full-rank factorization (see fuffrf) as

And consequently the initial dynamical system is equivalent to

or

Where w is now a white noise such as

Syntax [K,X]=lqe(Pw)

Computes the linear optimal LQ estimator gain K for the dynamical system

Where w is a white noise with unit covariance.

Examples

Assume the dynamical system formed by two masses connected by a spring and a damper:

A continuous time state space representation of this system is:

The instructions below can be used to compute a LQ state observer of the discretized version of this dynamical system.

// Form the state space model
M = 1; m = 0.2; k = 0.1; b = 0.004;
A = [  0     1    0    0
      -k/M  -b/M  k/M  b/M
       0     0    0    1
      k/m  b/m  -k/m  -b/m];
B = [0; 1/M; 0; 0];
C = [1 0 0 0  //dy1
     0 0 1 0];//dy2
//inputs u and e; outputs dy1 and dy2
P = syslin("c",A, B, C);
// Discretize it
dt=0.5;
Pd=dscr(P, dt);

//  Set the noise covariance matrices
Q_e=0.01; //additive input noise variance
R_vv=0.001*eye(2,2); //measurement noise variance
Q_ww=Pd.B*Q_e*Pd.B'; //input noise adds to regular input u

//----syntax [K,X]=lqe(P,Qww,Rvv [,Swv])---
Ko=lqe(Pd,Q_ww,R_vv); //observer gain

//----syntax [K,X]=lqe(P21)---
bigR =blockdiag(Q_ww, R_vv);
[W,Wt]=fullrf(bigR);
Bw=W(1:size(Pd.B,1),:);
Dw=W(($+1-size(Pd.C,1)):$,:);
Pw=syslin(Pd.dt,Pd.A,Bw,Pd.C,Dw);
Ko1=lqe(Pw); //same observer gain

//Check gains equality
norm(Ko-Ko1,1)

// Form the observer
O=observer(Pd,Ko);
//check stability
and(abs(spec(O.A))<1)
// Check by simulation
// Modify Pd to make it return the state
Pdx=Pd;Pdx.C=eye(4,4);Pdx.D=zeros(4,1);
t=0:dt:30;
u=zeros(t);
x=flts(u,Pdx,[1;0;0;0]);//state evolution
y=Pd.C*x;
// simulate the observer
x_hat=flts([u+0.01*rand(u);y+0.0001*rand(y)],O);
clf;
subplot(2,2,1)
  plot2d(t',[x(1,:);x_hat(1,:)]'),
  e=gce();e.children.polyline_style=2;
  L=legend('$x_1=dy_1$', '$\hat{x_1}$');L.font_size=3;
  xlabel('Time (s)')
subplot(2,2,2)
  plot2d(t',[x(2,:);x_hat(2,:)]')
  e=gce();e.children.polyline_style=2;
  L=legend('$x_2=dy_1^+$', '$\hat{x_2}$');L.font_size=3;
  xlabel('Time (s)')
subplot(2,2,3)
  plot2d(t',[x(3,:);x_hat(3,:)]')
  e=gce();e.children.polyline_style=2;
  L=legend('$x_3=dy_2$', '$\hat{x_3}$');L.font_size=3;
  xlabel('Time (s)')
subplot(2,2,4)
  plot2d(t',[x(4,:);x_hat(4,:)]')
  e=gce();e.children.polyline_style=2;
  L=legend('$x_4=dy_2^+$', '$\hat{x_4}$');L.font_size=3;
  xlabel('Time (s)')

Reference

Engineering and Scientific Computing with Scilab, Claude Gomez and al.,Springer Science+Business Media, LLC,1999, ISNB:978-1-4612-7204-5

See also

• lqr — LQ compensator (full state)
• observer — observer design
• obscont — observer based controller
• lqg — LQG compensator
• fullrf — factorisation de rang plein

History