Optymalizacja wzmocnien obserwatora Luenbergera silnika indukcyjnego metoda roju czastek

The paper describes experiments with the gain selection of an induction motor state observer, using particle swarm optimization (PSO) method. The research focused on comparing different versions of the PSO algorithm, optimizing the fitness function elaborated during preceding research. Three different learning methods were analyzed, GB (Global Best), LB (Local Best) and FIPS (Fully Informed Particle Swarm). The last two methods operate on the basis of a given swarm topology, to be selected from a ring lattice, Von Neumann lattice and FDR (Fitness Distance Ratio). The problems of convergence and stability of the algorithm, depending on parameters such as a cognition factor, were analyzed. The results for 1000 runs of PSO with 20 different sets of parameters were presented and compared.