LMIs in Control/Click here to continue/Observer synthesis/Reduced-Order State Observer
The Reduced Order State Observer design paradigm follows naturally from the design of Full Order State Observer.

where
,
,
, at any
.
- The matrices
are system matrices of appropriate dimensions and are known.
Given a State-space representation of a system given as above. First an arbitrary matrix
is chosen such that the vertical augmented matrix given as

is nonsingular, then

Furthermore, let

then the matrix pair
is detectable if and only if
is detectable, then let

then a new system of the form given below can be obtained

once an estimate of
is obtained the the full state estimate can be given as

the the reduced order observer can be obtained in the form.

Such that for arbitrary control and arbitrary initial system values, There holds

The value for
can be obtain by solving the following LMI.
The reduced-order observer exists if and only if one of the two conditions holds.
1) There exist a symmetric positive definite Matrix
and a matrix
that satisfy

Then 
2) There exist a symmetric positive definite Matrix
that satisfies the below Matrix inequality
Then
.
By using this value of
we can reconstruct the observer state matrices as

Hence, we are able to form a reduced-order observer using which we can back of full state information as per the equation given at the end of the problem formulation given above.
A list of references documenting and validating the LMI.
- LMIs in Control Systems Analysis, Design and Applications - Duan and Yu
- LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
- LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
- LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.