Third order transfer function response optimization with particle swarm algorithm

In this thesis, the use of PSO for the improvement of the 3rd order transfer function response is investigated. More specifically, the PSO algorithm is used to find the best control value that improves the maximum overshoot response of the 3rd order transfer function under step input. Furthermore, the factors affecting the performance of PSO are investigated. As a result of the studies carried out in this thesis, PSO is shown to be an effective method to improve the 3rd order transfer function response. The factors affecting the performance of PSO include the choice of objective function, number of particles, number of iterations and learning coefficients. Increasing the number of particles improves the performance of PSO. However, if the number of particles is too large, it can negatively affect the performance of PSO. Increasing the number of iterations also improves the performance of PSO. Similarly, if the number of iterations is too large, it can negatively affect the performance of PSO. Learning coefficients affect the speed and accuracy of PSO. Choosing the learning coefficients appropriately helps to improve the performance of PSO. This thesis will be useful for practitioners using PSO with 3rd order transfer functions. PSO is an alternative for many applications. Keywords; System, Algoritma, PSO, Transfer Function, Overshoot