Performance Review of Fractional Order PID (FOPID) Based Controllers Employed in Brushless DC Motor

Kumari, Sweety; Raj, Sonali; Kumar, Ramesh

doi:10.1109/parc49193.2020.236591

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Table (3) confirms the results of table (2) where the FOPID is better than PID control. Also the voltage and current of PID and FOPID shows no difference in figure (13,14). performing compared to another optimization was realized.…”

Section: Bldc Motormentioning

confidence: 95%

“…In 2019, Hanane Balaska, Samir Ladaci et al [13] presented a fractional order model reference adaptive control (FOMRAC) system which is assigned for the cruise control of a DC motor-based electric vehicle. The main contribution in this research article is to utilize fractional order reference adaptive controllers for both of two-layer control loops.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure(9,10,11,12,13,14) present Voltage of PID compared to FOPID on step response Generally, the previous relation does not offer the best result in terms of the optimization of the PID controller. By adding a variable , this optimization (maximization of integral gain ki) is improved in conditions of reaching better performance.…”

mentioning

confidence: 94%

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AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

Aboshosha

Hamed

2022

Preprint

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Within the next decades the Electric Vehicle (EV) will dominate the cars market, may be before 2040, where all countries will cope with the zero emissions policy of international institutions to reduce the destructive influence of the climate change. The EVs are simpler in construction and control compared with the Internal Combustion Engine (ICE) vehicles. EV’s speed is the most important factors to be controlled and optimized. Throughout this research article the Fractional Order PID (FOPID) controller has been used to regulate the speed of the EV while meta-heuristic techniques (AEFA, AEO, SMA, GOA, AND WCA) are used to optimize the performance of the EV using Brush Less Direct Motor (BLDC).

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Section: Bldc Motormentioning

confidence: 95%

Section: Literature Reviewmentioning

confidence: 99%

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AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

Aboshosha

Hamed

2022

Preprint

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“…In addition, these articles tackled the tuning gains of both PID and FOPID controllers with various optimization techniques in different domains and for different applications. Table .1 represents a comparison among these papers [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Review of Various Metaheuristics Techniques for Tuning Parameters of PID/FOPID Controllers

2022

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Alongside the rapid advancement of technology, researchers are looking for accurate, flexible, and efficient ways to control systems. One big breakthrough in technology is the PID and its following advancement which is FOPID controller. The PID controller is becoming widely used due to its simplicity and cost-effectiveness, while the latter is more desirable because of its accuracy and flexibility. The parameters of the aforementioned controllers have an enormous effect on the transient responses. For instance, the settling time, rising time, Integral Absolute Error (IAE), and overshooting are greatly influenced by the controller parameters. However, the most difficult problem in delivering the active case of the transient response of any system is the tuning of system parameters. Ziegler-Nicolas (ZN), recursive least squares (RLS) for tuning PID controller, while Pole Placement, and Pole Distribution for tuning FOPID are some examples of classical algorithms that are utilized previously to tackle this problem. Recently, the optimization methods have been employed to tune these parameters in order to have a more stable controller system. This systematic literature review (SLR) will take into consideration various optimization methods that tackled this problem. Methods like PSO, GA and ACO are the most utilized in tackling this problem. This paper will be an introductory guide to other researchers which will immensely help in their metaheuristic method selection.

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“…On the other hand, fractional calculus, as an incipient field in mathematics, has been extensively used in many branches of physics and engineering sciences, such as control systems [26][27][28][29][30], dynamical systems [31], viscoelastic materials [32][33][34], signal and image processing [35], diffusion wave [36], biomedical applications [37], stochastic and chaotic systems [38], and heat systems [39]. However, most studies published in the last three decades have concentrated on the fractional PID controller [40][41][42]. In recent years, there has been growing interest in controlling uncertain fractional-order nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties

2023

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The control of nonlinear chaotic systems with uncertainties is a challenging problem that has attracted the attention of researchers in recent years. In this paper, we propose a robust adaptive fuzzy fractional control strategy for stabilizing nonlinear chaotic systems with uncertainties. The proposed strategy combined a fuzzy logic controller with fractional-order calculus to accurately model the system’s behavior and adapt to uncertainties in real-time. The proposed controller was based on a supervised sliding mode controller and an optimal robust adaptive fractional PID controller subjected to fuzzy rules. The stability of the closed-loop system was guaranteed using Lyapunov theory. To evaluate the performance of the proposed controller, we applied it to the Duffing–Holmes oscillator. Simulation results demonstrated that the proposed control method outperformed a recently introduced controller in the literature. The response of the system was significantly improved, highlighting the effectiveness and robustness of the proposed approach. The presented results provide strong evidence of the potential of the proposed strategy in a range of applications involving nonlinear chaotic systems with uncertainties.

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Performance Review of Fractional Order PID (FOPID) Based Controllers Employed in Brushless DC Motor

Cited by 6 publications

References 23 publications

AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

Review of Various Metaheuristics Techniques for Tuning Parameters of PID/FOPID Controllers

Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties

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