Publication: Improved framework for balanced truncation based model reduction of second order structured systems
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Date
2022-08-01
Authors
Ali, Sadaqat
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Abstract
Model order reduction (MOR) techniques approximate the behavior of the large systems with lower order system information. The need to formulate algorithms for linear second order structured systems (SOSSs), bilinear SOSSs and nonlinear system arose, as there was not much literary support to encounter MOR especially for unstable systems. Two structure preserving second order balanced truncation (SOBT) techniques for unstable SOSSs as well as bilinear SOSSs along with an improved balanced truncation technique for nonlinear systems are implemented. Bernoulli feedback stabilization is applied and gramians are computed by solving infinite/finiteinterval algebraic Lyapunov equations. The gramians are partitioned into position and velocity portions for structure preservation and retention of original interpretation in reduced order model. Then gramians are balanced with different combinations to obtain SOBT techniques. To verify the correctness of the developed frameworks, all the proposed methods have been tested on several benchmark (building model, Piezoelectric and distillation column systems) and simulated systems. Results depict that limited interval techniques are much better than infinite interval techniques with average reduction error of 50% less. Moreover, techniques for combined time-frequency limited applications are also presented where average reduction error has been found 25% less than infinite interval techniques. The proposed frameworks can be applied to model order reduction applications related to unstable linear SOSSs, bilinear SOSSs, and nonlinear systems over infinite and finite time/frequency intervals.