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

Abstract

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 (λ and µ). 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 finetune 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.

Electronically Tunable ACO Based Fuzzy FOPID Controller for Effective Speed Control of Electric Vehicle

“…Hence, it is worth noting that the tuning of membership function can significantly enhance the system's performance. The Ant Colony Optimization (ACO) algorithm is preferred to optimize the controller parameters and tune the membership functions due to its numerous advantages compared to other optimization algorithms such as GA and PSO [38]. The ACO algorithm is a meta-heuristic approach that offers high robustness, better reliability, greater flexibility, fast convergence, easy implementation, and fewer optimization parameters [38]- [41].…”

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

“…Ant colony optimization (ACO), a new meta-heuristic algorithm has been used for the optimum solutions of power system problems like economic power dispatch and power system stabilization [20], [21]. Recently ACO algorithm has been used for optimal tuning of gain parameters to enhance the stability of multi-machine power systems [22] Also, ACO has been used to get the optimal parameters of controller for disturbance rejection in induction motor and magnetic levitation control system respectively [23], [24]. Therefore, it is decided to use ant colony optimization method in order to get optimum disturbance rejection in VSC -MTDC network.…”

Semi-Active Magnetic Levitation System for Education

“…The regulated current then produces a corresponding electromagnet force that keeps the object levitating at the desired position. As largely documented in the literature, controlling the object's position is difficult, mainly because the MagLev shows a nonlinear, unstable behavior (e.g., [11][12][13]).…”

“…A subsequent study has shown that the sliding-mode control outperforms the classical PID control [17]. Another study has also improved the traditional PID by introducing the socalled fractional-order PID control with a soft computing approach [11]. In [18], the authors present a comparison between the sliding-mode control and the fractional-order sliding mode control, emphasizing the benefits of the latter.…”