Model Order Reduction using Bio-inspired PSO and BFO Soft -Computing for Comparative Study

The authors proposes here a method for model order reduction of linear time invariant(LTI) dynamic system using two bio-inspired computational techniques, namely, Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). The numerator and denominator polynomial of the reduced order model of high order linear dynamic system are computed by minimizing the integral square error between the original high order and reduced order system using PSO and BFO. The two approaches find a solution to a given objective function employing different procedures and computational techniques; as a result their performance can be evaluated and compared. The problem area chosen is that of reduced order system modeling used in control systems engineering. Particle Swarm Optimization and Bacterial Foraging Optimization algorithm obtains a better lower order approximant that reflect the characteristics of the original higher order system and the performance evaluated using these methods are compared. Integral square error is used as an indicator for selecting the lower order model. The proposed method guarantees stability of the reduced order model, if the original high order system is stable. The method is illustrated with the help of an example using Mat lab 2010 environment.