LAUSR

Enns’ frequency weighted model reduction method yields an unstable reduced model. Many stability-preserving techniques for one-dimensional and two-dimensional reduced-order systems have been demonstrated; however, these methods produce significant truncation errors. This article presents a frequency weighted stability preserving framework, which addresses Enns’ main problem concerning reduced-order model instability. Unlike other stability-preserving techniques, the offered frameworks provide an easily computable a priori error-bound expression. The simulation results show that the proposed frameworks outperform existing stability-preserving approaches, demonstrating effectiveness.