Stability Preserving Model Reduction Technique for 1-D and 2-D Systems With Error Bounds

2-D state-space models are hard to deal with due to the complex structure; furthermore, simulation, analysis, design, and control will become more complicated when its order increases. In this brief, the decomposition of the 2-D model into two 1-D models are obtained by minimal rank-decomposition condition then the model reduction is performed on these two 1-D models. The proposed technique applies to both 1-D and 2-D systems. Furthermore, the proposed technique provides the reduced-order model’s stability, and an
a priori
error bound expression for the 1-D and 2-D systems. Numerical examples are presented along with comparisons among existing and the proposed techniques that illustrate the proposed technique’s efficacy.