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.

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A nano-grid is an independent hybrid sustainable framework that utilizes non-renewable and renewable power resources for supplying continuous electrical energy to the load. Considering this scenario, in this research work, photovoltaic (PV) array, wind turbine, and fuel cell are taken as the three generation resources that have been used in the nano-grid. The active and reactive power of the all three generation resources is controlled using various controllers, i.e. integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, fractional order proportional integral derivative (FOPID) and sliding mode controller (SMC). An advanced optimization technique based on a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers. The integral square error is taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes is evaluated to find the best optimal solution. The performance of SMC has surpassed the performance of all other optimized controllers for power stability. In less than 0.267 seconds, the actual power yielded by using SMC is within 1% of the desired power. PSO algorithm has performed better than GA algorithm with all controllers. The worst performance is by FOPID controller with a steady state error of 6071.3W using GA algorithm and have a high magnitude of overshoot and undershoot. Moreover, a smart switching algorithm has been introduced for switching between the generation resources in accordance with the load demand and cost of the system in order to operate the nano-grid more economically. Finally, a case study has been performed in which the smart switching algorithm is utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads.

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