Optimal L-infinity model reduction using genetic algorithms

In this paper, we are concerned with the solution of optimal L-infinity-mod el-reduction problems using genetic algorithms (GAs). More precisely, we pr esent an approach to facilitate using GAs to effectively search optimal red uced-order models for high-order linear time-invariant systems such that th e L-infinity-norm of approximation error is minimized. The proposed approac h has the following distinct features: (i) the parameter space for GA searc h is bounded- (ii) the finite and infinite zero structures, as well as the stability of the original system is retained in the reduced-order models; a nd (ii) the GA solution accuracy can be greatly enhanced. The first two fea tures are achieved through representing a reduced-order model in a transfer function parameterized in terms of Schur and anti-Schur polynomials, where as the third feature is gained by using schemes of region contraction and l ocal improvement.