An evolutionary computation based approach for reduced order modelling of linear systems

A new model order reduction algorithm taking the advantages of reciprocal transformation and principal pseudo break frequency estimation is presented. The denominator polynomial is constructed using the approximate dominant poles obtained. Ultimately the denominator polynomial formation is based on simple calculations involving high order system characteristic polynomial. Numerator polynomial is then determined using a recently proposed evolutionary computation algorithm-Big Bang Big Crunch algorithm. The method is simple and yields stable reduced order models. Difficulty may arise in finding complex poles in the reduced order model. However a modification in the algorithm by introducing search method to find the imaginary parts of such poles helps in overcoming this.

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