Optimized PID Based Controllers for Improving Transient and Steady State Response of Maglev System

Karahan O. , Ayyıldız B.

in: Advances in Engineering Research, Victoria M. Petrova, Editor, Nova Science Publishers, New-York, pp.149-185, 2020

  • Publication Type: Book Chapter / Chapter Research Book
  • Publication Date: 2020
  • Publisher: Nova Science Publishers
  • City: New-York
  • Page Numbers: pp.149-185
  • Editors: Victoria M. Petrova, Editor


Magnetic levitation (Maglev) systems play an important role in many applications widely used in different fields of industry such as electrical, automotive, aerospace and transportation engineerings. Control of the Maglev systems has been an extremely challenging task since the dynamics of Maglev is inherently unstable and nonlinear. Accordingly, the reaserchers have proposed different control approaches to provide high performance and robust control. In this chapter, 1-Degree of Freedom (1-DOF) and 2-Degree of Freedom (2-DOF) Proportional-Intergral-Derivative (PID) and Fractional Order PID (FOPID) controllers have been designed and applied for the control of Maglev system. The parameters of the 1 & 2-DOF PID and FOPID controllers have been tuned by Cuckoo Search (CS) algorithm based on the swarm intelligence approach. During the optimization, the different performance criteria as Integrated Absolute Error (IAE), Intgerated Time Weighted Absolute Error (ITAE), Integrated Squared Error (ISE) and Integrated Time Weighted Squared Error (ITSE) have been used to minimize the airgap error signal for a better stability and faster response. The performance of the optimized controllers has been compared with those of the PID based controllers optimized with different swarm intelligence algorithms and different optimization approaches in the literature for the same Maglev system in terms of maximum overshoot, rise time, settling time and steady state error. Finally, the simulation results are used to show the superiority of the CS algorithm in robust optimization controller tuning in this application and it can be considered as a good optimizing tool in the Maglev systems.