Efficient maximum power point tracking in fuel cell using the fractional-order PID controller

Rana, K.P.S.; Kumar, Vineet; Sehgal, Nitish; George, Sunitha; Azar, Ahmad Taher

doi:10.1016/b978-0-12-820004-9.00017-6

Cited by 8 publications

(3 citation statements)

References 83 publications

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“…In tandem, metaheuristic algorithms, such as genetic programming and particle swarm optimization, empower AI systems to solve complex problems by mimicking natural processes of optimization and exploration. Robotics, on the other hand, serves as the embodiment of AI in the physical world, leveraging computational intelligence and metaheuristics to create intelligent machines capable of autonomous action and interaction with their environment (Al Mhdawi et al, 2022;Toumi et al, 2022;Abed et al, 2022;Daraz et al, 2022Daraz et al, , 2021Mahdi et al, 2022;Najm et al, 2022;Abdul-Adheem et al, 2022, 2020aHumaidi et al, 2023Humaidi et al, , 2022Saidi et al, 2022;Ghoudelbourk et al, 2022Ghoudelbourk et al, , 2021Ghoudelbourk et al, , 2020Ghoudelbourk et al, , 2016Ajel et al, 2021;Bouchemha et al, 2021;Rana et al, 2021;Lajouad et al, 2021;Al-Qassar et al, 2021b;Hamiche et al, 2021;Kammogne et al, 2020;Alain et al, 2020;Ghazizadeh et al, 2018). Together, these components epitomize the multifaceted nature of AI, driving innovation and reshaping the boundaries of technological possibility.…”

Section: Related Workmentioning

confidence: 99%

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Nivetha,

Hannah Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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The difficulty in predicting early cancer is due to the lack of early illness indicators. Metaheuristic approaches are a family of algorithms that seek to find the optimal values for uncertain problems with several implications in optimization and classification problems. An automated system for recognizing illnesses can respond with accuracy, efficiency, and speed, helping medical professionals spot abnormalities and lowering death rates. This study proposes the Novel Hybrid GAO (Genetic Arithmetic Optimization algorithm based Feature Selection) (Genetic Arithmetic Optimization Algorithm-based feature selection) method as a way to choose the features for several machine learning algorithms to classify readily available data on COVID-19 and lung cancer. By choosing just important features, feature selection approaches might improve performance. The proposed approach employs a Genetic and Arithmetic Optimization to enhance the outcomes in an optimization approach.

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Section: Related Workmentioning

confidence: 99%

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Nivetha,

Hannah Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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“…The traditional PID (Proportional-Integral-Derivative) controller has a simple structure; it is widely used in the control community due to its simplicity, robustness, and familiarity (Ng et al, 2012;Nguyen et al, 2016;Pilla et al, 2021bPilla et al, , 2020Pilla et al, , 2019Rana et al, 2021;Najm et al, 2021c;Soliman et al, 2020;Sallam et al, 2020;Gorripotu et al, 2019;Ammar et al, 2018). However, it is not suitable for a large inertial system, and matching the optimal parameters is difficult.…”

Section: Design Of the Proposed Controlmentioning

confidence: 99%

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

Saidi¹,

Mellah²,

Fekik

et al. 2022

International Journal of Service Science, Management, Engineering, and Technology

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In this work, the differential mobile robot is controlled utilizing fuzzy PID speed control, which combines fuzzy control with conventional PID control in real time. The path may be convoluted, and the surrounding environment may contain a range of arbitrary shape and size obstacles. A monocular camera is used to detect obstacles during the navigation process. To enable a robot to travel within an indoor space while avoiding obstacles, a basic image processing approach based on area of interest was used. The goal of this research is to develop a fuzzy PID speed controller on a real robot, as well as a simple and efficient visual obstacle avoidance system. MATLAB is used to implement the control system. GUIDE (graphical user interface development environment) has enabled the creation of graphical user interfaces. These interfaces make it easy to manipulate the system in real time and capture live video. The proposed methodologies are tested on a non-holonomic dr robot i90 mobile robot, and the results are satisfactory.

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“…This makes the PV cell operating point change as the irradiation or temperature and even the load change and it will not operate at the maximum power point (MPP). Therefore, a controlling technique has been proposed in the last decade under the name of maximum power point tracking (MPPT) [1][2][3][4][5][6][7][8][9]. Recently, several control techniques have been proposed as MPPT to extract and track the MPP such as the perturb and observe method (P&O) [10][11][12]; the incremental conductance method (IC) [10,13,14]; the constant voltage and constant current technique, which are known as the classical MPPT control technique [10]; and MPPT based on the control theory such as fuzzy control [15], neural network [16], optimal control [17], and robust control [18].…”

Section: Introductionmentioning

confidence: 99%

A New Nonlinear Controller for the Maximum Power Point Tracking of Photovoltaic Systems in Micro Grid Applications Based on Modified Anti-Disturbance Compensation

Azar

Abed

Abdulmajeed³

et al. 2022

Sustainability

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In the photovoltaic system, the performance, efficiency, and generated power of the PV system are affected by changes in the environment, disturbances, and parameter variations, and this leads to a deviation from the operating maximum power point (MPP) of the PV system. Therefore, the main aim of this paper is to ensure the PV system operates at the maximum power point under the influence of exogenous disturbances and uncertainties, i.e., no matter how the irradiation, temperature, and load of the PV system change, by proposing a maximum power point tracking for the photovoltaic system (PV) based on the active disturbance rejection control (ADRC) paradigm. The proposed method provides better performance with excellent tracking for the MPP by controlling the duty cycle of the DC–DC buck converter. Moreover, comparison simulations have been performed between the proposed method and the linear ADRC (LADRC), conventional ADRC, and the improved ADRC (IADRC) to investigate the effectiveness of the proposed method. Finally, the simulation results validated the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with excellent response and minimum output performance index (OPI).

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Efficient maximum power point tracking in fuel cell using the fractional-order PID controller

Cited by 8 publications

References 83 publications

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images

Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

A New Nonlinear Controller for the Maximum Power Point Tracking of Photovoltaic Systems in Micro Grid Applications Based on Modified Anti-Disturbance Compensation

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