Performance enhancement of magnetic levitation system using teaching learning based optimization

Abstract

This paper demonstrates the potency of evolution based optimization techniques in the sense of enhancing the system's performance. Teaching Learning Based Optimization (TLBO) is a well-known evolutionary algorithm used to optimize the parameters of the PID controller so as to improve the performance of the magnetic levitation system. The TLBO search algorithm is split into two phases, the teacher phase and the learner phase. The teacher phase is comprised of having minimum performance index as compared to learner phase. The learners improve their knowledge on the basis of teacher's performance. The parameters are tuned while minimizing the performance index of the system. The performance index incorporated in this paper is the integral time weighted square error (ITSE). The corroboration of the above technique is ended by comparing it with the conventional control techniques. © 2017 Faculty of Engineering, Alexandria University