Design and Control of Magnetic Levitation System by Optimizing Fractional Order PID Controller Using Ant Colony Optimization Algorithm

• Published in: IEEE access Vol. 8; pp. 116704 - 116723
• Main Authors: Mughees, Abdullah, Mohsin, Syed Ali
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerospace electronics; Algorithms; Ant colony optimization; Control stability; Control systems; Control systems design; Controllers; Design; Ferromagnetism; first principle modeling; First principles; FOPID controller; Fractional calculus; Maglev mathematical model; Magnetic levitation; Magnetic levitation systems; Mathematical model; Mathematical models; MATLAB-simulink; Neural networks; Order parameters; Proportional integral derivative; Routh-Hurwitz stability; Stability analysis; Stability criteria
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3004025

MAGnetic LEVitation (Maglev) is a multi-variable, non-linear and unstable system that is used to levitate a ferromagnetic object in free space. This paper presents the stability control of a levitating object in a magnetic levitation plant using Fractional order PID (FOPID) controller. Fractional calculus, which is used to design the FOPID controller, has been a subject of great interest over the last few decades. FOPID controller has five tunning parameters including two fractional-order parameters (<inline-formula> <tex-math notation="LaTeX">\lambda </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>). The mathematical model of the Maglev plant is obtained by using first principle modeling and the laboratory model (CE152). Maglev plant and FOPID controller both have been designed in MATLAB-Simulink. The designed model of the Maglev system can be further used in the process of controller design for other applications. The stability of the proposed system is determined via the Routh Hurwitz stability criterion. Ant Colony Optimization (ACO) algorithm and Ziegler Nichols method has been used to fine-tune the parameters of FOPID controller. FOPID controller output results are compared with the traditional IOPID controller for comparative analysis. FOPID controller, due to its extra tuned parameters, has shown extremely efficient results in comparison to the traditional IOPID controller.