Optimal Output Feedback
LMI
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Optimal output feedback control is a problem which arises from not knowing all information about the output of the system. It correlates to the state feedback situation where the part of the state is unknown. This issue can arise in decentralized control problems, for example, and requires the use of an "observer-like" solution. One such method is the use of a Kalman Filter, a more classical technique. However, other methods exist that do not implement a Kalman Filter such as the one below which uses an LMI to preform the output feeback. The
control methods form an optimization problem which attempts to minimize the
norm of the system.
The system is represented using the 9-matrix notation shown below.

where
is the state,
is the regulated output,
is the sensed output,
is the exogenous input, and
is the actuator input, at any
.
,
,
,
,
,
,
,
,
are known.
The LMI: Optimal Output Feedback
Control LMI
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The following are equivalent.
1) There exists a
such that
2) There exists
,
,
,
,
,
,
such that


The above LMI determines the the upper bound
on the
norm. In addition to this the controller
can also be recovered.




where,
![{\displaystyle {\begin{bmatrix}A_{K2}&B_{K2}\\C_{K2}&D_{K2}\end{bmatrix}}={\begin{bmatrix}X_{2}&X_{1}B_{2}\\0&I\end{bmatrix}}^{-1}\left[{\begin{bmatrix}A_{n}&B_{n}\\C_{n}&D_{n}\end{bmatrix}}-{\begin{bmatrix}X_{1}AY_{1}&0\\0&0\end{bmatrix}}\right]{\begin{bmatrix}Y_{2}^{T}&0\\C_{2}Y_{1}&I\end{bmatrix}}^{-1}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/4ca1e5f43bb9edaeaf5e3cad8cd2259b57567f96)
for any full-rank
and
such that
.
This implementation requires Yalmip and Sedumi.
https://github.com/eoskowro/LMI/blob/master/OF_Hinf.m
Optimal Output Feedback H2