Accurate model reduction of large scale systems using adaptive multi-objective particle swarm optimization algorithm

Due to existence of many variables in large scale systems, design and implementation of controllers for such systems have been always important challenges. A common way to solve the problem is to obtain an equivalent reduced order model of the system in the first step and then, design a suitable controller. In this paper model reduction of large scale systems based on multi objective particle swarm optimization is presented. According to the required order of reduction, proper reduced system is obtained. For high precision in modeling, multi objective particle swarm optimization is used. In this algorithm, multi objects regarding to properties of system response are solved. It is shown that proposed approach is very useful tool for model reduction of large scale systems. Simulation examples prove this claim.