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