“MODEL ORDER REDUCTION PROBLEM OF SINGLE INPUT AND SINGLE OUTPUT FOR CHEMO-INSPIRED GENETIC ALGORITHM”

Abstract

 The model order reduction (MOR) is a critical field in control system engineering. The construction of a lower order system with similar dynamic properties and behaviour of a higher order system in such a manner that the former is less complex than the later. Many engineering applications make use of higher-order SISO systems, which creates computation problems with the system analysis, controller design and simulation. This research paper proposes a fresh method for addressing the issue of model order reduction. The use of optimized genetic algorithm has been proposed to use chemo-inspired methods such as chemical reaction behaviour, molecular interaction and adaptive mutation schemes.   The system’s reduced model is obtained by minimizing system while maintaining stability and accuracy. The proposed method is evaluated on various standard benchmark transfer function models. settling time, rise time and frequency response characteristics are studied in various cases.  A reduced order model with high accuracy is the result of a genetic optimization technique inspired by chemotherapy. The results of the approximation are also better than existing reduction methods. The method proposed is straightforward and flexible and possesses considerable capability to build complex engineering control systems.