Optimization of Fractional and Integer Order PID Parameters using Big Bang Big Crunch and Genetic Algorithms for a MAGLEV System

Altintas, Gokhan; Aydın, Yücel

doi:10.1016/j.ifacol.2017.08.978

Cited by 26 publications

(9 citation statements)

References 13 publications

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“…Big-Bang Big-crunch (BBBC) and its hybrid model with the Genetic Algorithm (GA) have been used with the FOPID controller for controlling the Maglev system. Results show that the performance of the BBBC based FOPID controller is higher than that of the GA-optimized FOPID controller [87].…”

Section: Introductionmentioning

confidence: 94%

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

Mughees

Mohsin

2020

IEEE Access

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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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Section: Introductionmentioning

confidence: 94%

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

Mughees

Mohsin

2020

IEEE Access

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“…Proportional integral and derivative (PID) has been utilized to control the maglev system, which for so many years has been used in the industrial field. PID controllers have been utilized since the 1890s for controller design [9]- [12]. Until today the industrial field still uses PID controllers with other optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach

Abdalhadi

Wahid

Burhanuddin

2022

IJEECS

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<span>A magnetic levitation system (MLS) is a complex nonlinear system that requires an electromagnetic force to levitate an object in the air. The electromagnetic field is extremely sensitive to noise which can cause the acceleration on the spherical object, leading it to move into the unbalanced region. This paper presents a comparative assessment of controllers for the magnetic levitation system using proportional integral derivative (PID) controller based optimal tuning. The analysis was started by deriving the mathematical model followed by the implementation of radial basis function neural network (<a name="_Hlk93327650"></a>RBFNN) based metamodel. The optimal tuning of the PID controller has offered better transient responses with the improvement of overshoot and the rise time as compared to the standard optimization methods. It is more robust and tolerant as compared to gradient descent method. The simulation output using the radial basis based metamodel approach showed an overshoot of 9.34% and rise time of 9.84 ms, which are better than the gradient descent (GD) and conventional PID methods. For the verification purpose, a Simscape model has been developed which mimic the real model. It was found that the model has produced about similar performance as what has been obtained from the MATLAB simulation.</span>

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“…A satisfactory response of the converter requires a tunning of controller parameters whatever its type: PID or FOPID. In literature, many algorithms have been proposed for tunning controller parameters such as Genetic Algorithm (GA) [25], Cuckoo Search Algorithm (CS) [26], Chaotic Ant Swarm Algorithm (CAS) [27], grey wolf optimization (GWO) [28], Water Cycle Algorithm (WCA) [9], sine cosine algorithm (SCA) [29], Gradient-Based Optimization (GBO) [21] and Particle Swarm Optimization (PSO) [30]. Among the cited algorithms, PSO has been chosen for the control of our converter.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Fractional Order PI Controller by PSO Algorithm Applied to a Grid-Connected Photovoltaic System

Bouderres¹,

Kerdoun²,

Djellad³

et al. 2022

JESA

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With the increasing integration of renewable energies into power grids, their control and power quality are becoming the main focus of many research efforts. In a grid-connected photovoltaic system, the control strategy is necessary to efficiently use the solar energy as well as to ensure high power quality. This paper presents a study on the robustness of a Fractional Order PI controller based on the Particle Swarm Optimization algorithm (PSO-FOPI) in a grid-connected PV system. The controller used was integrated into the inverter to apply voltage-oriented control (VOC). Fractional order controllers have an additional degree of freedom, so that a wider range of parameters is available to provide better control. Parameter optimization of the FOPI and classical PI controllers are performed using the PSO algorithm. The performance of the FOPI controller is compared with that of the classical PI controller. A complete study of the behavior of the grid connected PV system is tested using MATLAB/Simulink. The simulation results show the performance and efficiency of the PSO-FOPI controller compared to the classical PI controller in terms of rapidity, stability and precision, as well as the THD reduction of the current injected to the grid for any variation of solar irradiance.

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Optimization of Fractional and Integer Order PID Parameters using Big Bang Big Crunch and Genetic Algorithms for a MAGLEV System

Cited by 26 publications

References 13 publications

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

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

An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach

Optimization of Fractional Order PI Controller by PSO Algorithm Applied to a Grid-Connected Photovoltaic System

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