LMIs in Control/Applications/Hinf optimal Model Reduction

Given a full order model and an initial estimate of a reduced order model it is possible to obtain a reduced order model optimal in sense. This methods uses LMI techniques iteratively to obtain the result.


The System

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Given a state-space representation of a system   and an initial estimate of reduced order model  .

 

Where   and  . Where   are full order, reduced order, number of inputs and number of outputs respectively.

The Data

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The full order state matrices   and the reduced model order  .

The Optimization Problem

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The objective of the optimization is to reduce the   norm distance of the two systems. Minimizing   with respect to  .

The LMI: The Lyapunov Inequality

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Objective:  .

Subject to::  

 

It can be seen from the above LMI that the second matrix inequality is not linear in  . By making   constant it is linear in  . And if   are constant it is linear in  . Hence the following iterative algorithm can be used.

(a) Start with initial estimate   obtained from techniques like Hankel-norm reduction/Balanced truncation.

(b) Fix   and optimize with respect to  .

(c) Fix   and optimize with respect to  .

(d) Repeat steps (b) and (c) until the solution converges.

Conclusion:

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The LMI techniques results in model reduction close to the theoretical limits set by the largest removed hankel singular value. The improvements are often not significant to that of Hankel-norm reduction. Due to high computational load it is recommended to only use this algorithm if optimal performance becomes a necessity.


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A list of references documenting and validating the LMI.


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