Practical Application of Fractional-Order PID Controller based on Evolutionary Optimization Approach for a Magnetic Levitation System

This article proposes a state-of-the-art design procedure of integer-order PID (IOPID) and fractional-order PID (FOPID) controller applied to a well-established and diversified engineering application of the Magnetic Levitation System (Maglev). Controller design and implementation for the Maglev system are quite complicated and difficult since the system dynamics exhibits non-linearity with a wide variation of operating points. Also, the system is highly unstable which rules out the possibility to accomplish conventional tuning techniques. Thus in this work, the controller tuning methodology is framed as a complex optimization problem by incorporating a new transient specification-based objective function. For designing and tuning of proposed controller parameters, modern meta-heuristic and evolutionary optimization algorithms are deployed; those are Bird Swarm Algorithm, Elephant Herding Optimization, Equilibrium optimizer and Grey Wolf Optimization. The software and hardware results demonstrate that FOPID controllers yield better time-domain and frequency-domain performance specifications and exhibit excellent reference tracking capability than IOPID controllers. The performance robustness of the proposed controllers is greatly enhanced subjected to a vast range of parametric uncertainties along with a significant minimization of the control effort.