Specifications

10 Design Case Studies
10-52
'outputname','y');
Assuming that , you can now design the discrete Kalman filter by
Q = 1; R = 1;
[kalmf,L,P,M] = kalman(Plant,Q,R);
This returns a state-space model kalmf of the filter as well as the innovation
gain
M
M =
3.7980e-01
8.1732e-02
-2.5704e-01
The inputs of kalmf are and , and its outputs are the plant output and
state estimates and .
Becauseyouareinterestedintheoutputestimate ,keeponly thefirstoutput
of
kalmf.Type
kalmf = kalmf(1,:);
kalmf
a =
x1_e x2_e x3_e
x1_e 0.7683 -0.494 0.1129
x2_e 0.6202 0 0
x3_e -0.081732 1 0
b =
u y
x1_e -0.3832 0.3586
QR1==
uy
v
y
e
y
ˆ
nn
[]
= x
ˆ
nn
[]
kalmf
Kalman filter
u
x
ˆ
nn
[]
y
v
y
e
y
e