Complex and high-order systems are used to express the many technical and scientific applications. The complexity of modelling, evaluating, and regulating these systems is where they face their biggest challenges. More physical insights may be gleaned from simpler models more easily than from more complicated models, which also provide lower-ordering controllers that are simpler to use. The high degree of computational complexity associated with such issues was reduced using the model order reduction (MOR), which was subsequently further enhanced for usage with growing amounts of CDS. To develop a simplified representation of the whole system and a controller based on Linear Quadratic Regulators (LQRs), a novel The abac-Whale Algorithm for Optimization of Artificial Bee Colonies (WOA) approach has been developed in this study. The mod makes use of the combined ABC-WOA benefits. When an algorithm has quick an approach to convergence with few control inputs, straightforward execution, and avoids situations that are either rare or harsh in their immediate vicinity, it becomes superb. First, effective parameters of reduced-order models for initial systems with a higher grade of complexity based on the ABC-WOA are derived. Then, a model with fewer degrees of freedom -optimized parameters for the linear quadratic regulator (LQR) controller. A statistical illustration is used to assess the efficacy of the suggested strategy. The experimental findings show that the lower order model of the suggested method features an excellent value for the near rough estimate of the actual system.

Whale Algorithm; Linear Quadratic Regulator; ABC Algorithm; Model Order Reduction; equation; Optimization