In the present work, a new framework is proposed to design controllers using model order reduction techniques for linear time invariant complex engineering systems. The proposed model order reduction methodology employs optimization-based techniques namely ant lion optimization and moth flame optimization for which boundary conditions are systematically procured from an interim model derived using balancing free square-root algorithm. An area control coefficient is introduced to adjust the exploration range of the optimization process around the coefficients of the interim reduced-order model. The numerator as well as denominator coefficients of the desired reduced-order models are optimized to retain the performance characteristics of the original high-order systems. The effectiveness of the proposed approach is assessed based on different error metrics and unit step response plots. To validate the performance, seven benchmark systems of different pole configurations have been considered from the literature. It has been found that proposed approach provides reduced-systems with significant improvement in error and transient performance when compared to the literature work. The suggested model order reduction approach is further extended to design proportional-integral-derivative controller and fractional-order proportional-integral-derivative controller for an 84th-order benchmark system and a mechanical ventilator system respectively. The results demonstrate that the proposed model order reduction-based controller design approach achieves high-performance control with lesser steady-state error, improved time-domain specifications and robust disturbance rejection capability.
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