Abstract
This paper proposed a new methodology to address the key problem in model order reduction methods of setting random values of lower & upper bounds and initial values of the parameters in optimization-based approaches. The moth flame optimization (MFO) method is utilized for the model order reduction process wherein the search space boundaries are found using a novel strategy with the classical balanced truncation technique. Both the numerator and denominator coefficients of the desired reduced-order system are found using the proposed optimization approach. The integral square error (ISE) is employed as the objective function in the optimization of SISO systems while a novel objective function is framed using ISE for the MIMO systems. The key advantage of using balanced truncation-based search space boundaries ensures targeted search with potential solutions and stability of the reduced order model. Further, the disadvantage of steady-state error of the balanced truncation is overcome using a gain adjustment factor. The overall methodology takes very less simulation time while keeping all the necessary parameters of the reduced-system close to those of the original system. To test the efficacy of the proposed methodology, five real-world high-order systems with two SISO systems, two MIMO systems and one discrete-time system are considered and compared with existing methods through several error indices and time and frequency-domain specifications. It has been found that the proposed methodology results in significant reduction of ISE and improvement in matching of step responses, preserving stability of the reduced-order models.