Order Reduction of Linear Interval Systems Using Particle Swarm Optimization

In recent years, genetic algorithms (GA) and particle swarm optimization (PSO) techniques have attracted considerable attention among various modern heuristic optimization techniques. In this paper PSO is employed for finding stable reduced order models of large-scale linear Interval systems. In this algorithm the numerator and denominator polynomials are determined by minimizing the Integral square error (ISE) between original and reduced model pertaining to unit step input by using PSO. The algorithm is simple, rugged and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. A numerical example illustrates the proposed algorithm.