H2 optimal model reduction using Genetic Algorithms and Particle Swarm Optimization

Modern engineering systems such as telecommunication systems, transmission lines, and chemical reactors are complex in nature. Their detailed mathematical modeling leads to high order dynamic systems. For simplicity of simulation, interpretation, and control of such processes it is desirable to represent the dynamics of these high order systems by lower order models. However, most of the available optimal model reduction techniques follow computationally demanding, time consuming, iterative procedures that usually result in non-robustly stable models with poor frequency response resemblance to the original high order model in some frequency ranges. Genetic algorithm (GA) and particle swarm optimization (PSO) are two of the most powerful optimization tools. Therefore, the aim of this paper will be to use GA and PSO to solve H₂ norm model reduction problems, and help obtain globally optimized nominal models.