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Varshney T, Waghmare AV, Singh VP, Ramu M, Patnana N, Meena VP, Azar AT, Hameed IA. Investigation of rank order centroid method for optimal generation control. Sci Rep 2024; 14:11267. [PMID: 38760466 DOI: 10.1038/s41598-024-61945-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/12/2024] [Indexed: 05/19/2024] Open
Abstract
Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher-learner based optimization algorithm (TLBOA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller's performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.
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Affiliation(s)
- T Varshney
- Department of EECE, Sharda University, Greater Noida, Uttar Pradesh, India
| | - A V Waghmare
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India
| | - V P Singh
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India
| | - M Ramu
- Department of ECE, GITAM University, Vizag, Andhra Pradesh, India
| | - N Patnana
- Department of ECE, GITAM University, Vizag, Andhra Pradesh, India
| | - V P Meena
- Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India.
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
- Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia.
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt.
| | - Ibrahim A Hameed
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Larsgardsvegen, 2, 6009, Alesund, Norway.
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Mathur A, Kumari R, Meena VP, Singh VP, Azar AT, Hameed IA. Data-driven optimization for microgrid control under distributed energy resource variability. Sci Rep 2024; 14:10806. [PMID: 38734728 PMCID: PMC11088685 DOI: 10.1038/s41598-024-58767-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 04/03/2024] [Indexed: 05/13/2024] Open
Abstract
The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid's real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.
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Affiliation(s)
- Akhilesh Mathur
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India
| | - Ruchi Kumari
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India
| | - V P Meena
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India.
- Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India.
| | - V P Singh
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
- Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh, Saudi Arabia.
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt.
| | - Ibrahim A Hameed
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Larsgardsvegen, 2, 6009, Alesund, Norway.
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Reshan MSA, Amin S, Zeb MA, Sulaiman A, Alshahrani H, Azar AT, Shaikh A. Enhancing Breast Cancer Detection and Classification Using Advanced Multi-Model Features and Ensemble Machine Learning Techniques. Life (Basel) 2023; 13:2093. [PMID: 37895474 PMCID: PMC10608611 DOI: 10.3390/life13102093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
Breast cancer (BC) is the most common cancer among women, making it essential to have an accurate and dependable system for diagnosing benign or malignant tumors. It is essential to detect this cancer early in order to inform subsequent treatments. Currently, fine needle aspiration (FNA) cytology and machine learning (ML) models can be used to detect and diagnose this cancer more accurately. Consequently, an effective and dependable approach needs to be developed to enhance the clinical capacity to diagnose this illness. This study aims to detect and divide BC into two categories using the Wisconsin Diagnostic Breast Cancer (WDBC) benchmark feature set and to select the fewest features to attain the highest accuracy. To this end, this study explores automated BC prediction using multi-model features and ensemble machine learning (EML) techniques. To achieve this, we propose an advanced ensemble technique, which incorporates voting, bagging, stacking, and boosting as combination techniques for the classifier in the proposed EML methods to distinguish benign breast tumors from malignant cancers. In the feature extraction process, we suggest a recursive feature elimination technique to find the most important features of the WDBC that are pertinent to BC detection and classification. Furthermore, we conducted cross-validation experiments, and the comparative results demonstrated that our method can effectively enhance classification performance and attain the highest value in six evaluation metrics, including precision, sensitivity, area under the curve (AUC), specificity, accuracy, and F1-score. Overall, the stacking model achieved the best average accuracy, at 99.89%, and its sensitivity, specificity, F1-score, precision, and AUC/ROC were 1.00%, 0.999%, 1.00%, 1.00%, and 1.00%, respectively, thus generating excellent results. The findings of this study can be used to establish a reliable clinical detection system, enabling experts to make more precise and operative decisions in the future. Additionally, the proposed technology might be used to detect a variety of cancers.
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Affiliation(s)
- Mana Saleh Al Reshan
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; (M.S.A.R.); (A.S.)
| | - Samina Amin
- Institute of Computing, Kohat University of Science and Technology, Kohat 26000, Pakistan; (S.A.); (M.A.Z.)
| | - Muhammad Ali Zeb
- Institute of Computing, Kohat University of Science and Technology, Kohat 26000, Pakistan; (S.A.); (M.A.Z.)
| | - Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; (A.S.); (H.A.)
| | - Hani Alshahrani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; (A.S.); (H.A.)
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia; (M.S.A.R.); (A.S.)
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Alshahrani H, Sharma G, Anand V, Gupta S, Sulaiman A, Elmagzoub MA, Reshan MSA, Shaikh A, Azar AT. An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder. Life (Basel) 2023; 13:2091. [PMID: 37895472 PMCID: PMC10607952 DOI: 10.3390/life13102091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
Bone marrow (BM) is an essential part of the hematopoietic system, which generates all of the body's blood cells and maintains the body's overall health and immune system. The classification of bone marrow cells is pivotal in both clinical and research settings because many hematological diseases, such as leukemia, myelodysplastic syndromes, and anemias, are diagnosed based on specific abnormalities in the number, type, or morphology of bone marrow cells. There is a requirement for developing a robust deep-learning algorithm to diagnose bone marrow cells to keep a close check on them. This study proposes a framework for categorizing bone marrow cells into seven classes. In the proposed framework, five transfer learning models-DenseNet121, EfficientNetB5, ResNet50, Xception, and MobileNetV2-are implemented into the bone marrow dataset to classify them into seven classes. The best-performing DenseNet121 model was fine-tuned by adding one batch-normalization layer, one dropout layer, and two dense layers. The proposed fine-tuned DenseNet121 model was optimized using several optimizers, such as AdaGrad, AdaDelta, Adamax, RMSprop, and SGD, along with different batch sizes of 16, 32, 64, and 128. The fine-tuned DenseNet121 model was integrated with an attention mechanism to improve its performance by allowing the model to focus on the most relevant features or regions of the image, which can be particularly beneficial in medical imaging, where certain regions might have critical diagnostic information. The proposed fine-tuned and integrated DenseNet121 achieved the highest accuracy, with a training success rate of 99.97% and a testing success rate of 97.01%. The key hyperparameters, such as batch size, number of epochs, and different optimizers, were all considered for optimizing these pre-trained models to select the best model. This study will help in medical research to effectively classify the BM cells to prevent diseases like leukemia.
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Affiliation(s)
- Hani Alshahrani
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia; (H.A.); (A.S.)
| | - Gunjan Sharma
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India; (G.S.); (V.A.); (S.G.)
| | - Vatsala Anand
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India; (G.S.); (V.A.); (S.G.)
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India; (G.S.); (V.A.); (S.G.)
| | - Adel Sulaiman
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia; (H.A.); (A.S.)
| | - M. A. Elmagzoub
- Department of Network and Communication Engineering, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia;
| | - Mana Saleh Al Reshan
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia; (M.S.A.R.); (A.S.)
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia; (M.S.A.R.); (A.S.)
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia
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Hameed IA, Abbud LH, Abdulsaheb JA, Azar AT, Mezher M, Jawad AJM, Abdul-Adheem WR, Ibraheem IK, Kamal NA. A New Nonlinear Dynamic Speed Controller for a Differential Drive Mobile Robot. Entropy (Basel) 2023; 25:514. [PMID: 36981402 PMCID: PMC10048643 DOI: 10.3390/e25030514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/08/2023] [Accepted: 03/11/2023] [Indexed: 06/18/2023]
Abstract
A disturbance/uncertainty estimation and disturbance rejection technique are proposed in this work and verified on a ground two-wheel differential drive mobile robot (DDMR) in the presence of a mismatched disturbance. The offered scheme is the an improved active disturbance rejection control (IADRC) approach-based enhanced dynamic speed controller. To efficiently eliminate the effect produced by the system uncertainties and external torque disturbance on both wheels, the IADRC is adopted, whereby all the torque disturbances and DDMR parameter uncertainties are conglomerated altogether and considered a generalized disturbance. This generalized disturbance is observed and cancelled by a novel nonlinear sliding mode extended state observer (NSMESO) in real-time. Through numerical simulations, various performance indices are measured, with a reduction of 86% and 97% in the ITAE index for the right and left wheels, respectively. Finally, these indices validate the efficacy of the proposed dynamic speed controller by almost damping the chattering phenomena and supplying a high insusceptibility in the closed-loop system against torque disturbance.
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Affiliation(s)
- Ibrahim A. Hameed
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Larsgårdsve-gen, 2, 6009 Ålesund, Norway
| | - Luay Hashem Abbud
- Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq
| | - Jaafar Ahmed Abdulsaheb
- Department of Electronics and Communication, College of Engineering, Uruk University, Baghdad 10001, Iraq
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
| | - Mohanad Mezher
- Faculty of Pharmacy, The University of Mashreq, Baghdad 10001, Iraq
| | | | | | - Ibraheem Kasim Ibraheem
- Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad 10001, Iraq
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Hussain MM, Azar AT, Ahmed R, Umar Amin S, Qureshi B, Dinesh Reddy V, Alam I, Khan ZI. SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks. Sensors (Basel) 2023; 23:667. [PMID: 36679463 PMCID: PMC9866253 DOI: 10.3390/s23020667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/26/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay-energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.
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Affiliation(s)
- Md. Muzakkir Hussain
- Department of Computer Science and Engineering, SRM University, Amaravati 522502, India
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt
| | - Rafeeq Ahmed
- Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
| | - Syed Umar Amin
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Basit Qureshi
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - V. Dinesh Reddy
- Department of Computer Science and Engineering, SRM University, Amaravati 522502, India
| | - Irfan Alam
- Department of Computer Science and Engineering, Delhi Technological University, Delhi 110042, India
| | - Zafar Iqbal Khan
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
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Ganesan J, Inbarani HH, Azar AT, Polat K. Retraction Note: Tolerance rough set firefly-based quick reduct. Neural Comput Appl 2023. [DOI: 10.1007/s00521-022-08197-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Ahmed S, Azar AT, Tounsi M. Design of Adaptive Fractional-Order Fixed-Time Sliding Mode Control for Robotic Manipulators. Entropy (Basel) 2022; 24:e24121838. [PMID: 36554243 PMCID: PMC9778649 DOI: 10.3390/e24121838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 05/28/2023]
Abstract
In this investigation, the adaptive fractional-order non-singular fixed-time terminal sliding mode (AFoFxNTSM) control for the uncertain dynamics of robotic manipulators with external disturbances is introduced. The idea of fractional-order non-singular fixed-time terminal sliding mode (FoFxNTSM) control is presented as the initial step. This approach, which combines the benefits of a fractional-order parameter with the advantages of NTSM, gives rapid fixed-time convergence, non-singularity, and chatter-free control inputs. After that, an adaptive control strategy is merged with the FoFxNTSM, and the resulting model is given the label AFoFxNTSM. This is done in order to account for the unknown dynamics of the system, which are caused by uncertainties and bounded external disturbances. The Lyapunov analysis reveals how stable the closed-loop system is over a fixed time. The pertinent simulation results are offered here for the purposes of evaluating and illustrating the performance of the suggested scheme applied on a PUMA 560 robot.
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Affiliation(s)
- Saim Ahmed
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
| | - Mohamed Tounsi
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia
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Ashfaq T, Khalid R, Yahaya AS, Aslam S, Azar AT, Alsafari S, Hameed IA. A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism. Sensors (Basel) 2022; 22:7162. [PMID: 36236255 PMCID: PMC9572131 DOI: 10.3390/s22197162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 09/11/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms-XGboost and random forest (RF)-used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.
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Affiliation(s)
- Tehreem Ashfaq
- Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
| | - Rabiya Khalid
- Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
| | - Adamu Sani Yahaya
- Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
- Department of Information Technology, Bayero University Kano, Kano 700006, Nigeria
| | - Sheraz Aslam
- Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
- Automated Systems & Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 12435, Saudi Arabia
| | - Ahmad Taher Azar
- Automated Systems & Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 12435, Saudi Arabia
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
| | - Safa Alsafari
- Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
| | - Ibrahim A. Hameed
- Department of ICT and Natural Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
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Ashfaq T, Khalid R, Yahaya AS, Aslam S, Azar AT, Alkhalifah T, Tounsi M. An Intelligent Automated System for Detecting Malicious Vehicles in Intelligent Transportation Systems. Sensors (Basel) 2022; 22:6318. [PMID: 36080777 PMCID: PMC9460212 DOI: 10.3390/s22176318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/09/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
The exponential growth of intelligent vehicles(IVs) development has resulted in a complex network. As the number of IVs in a network increases, so does the number of connections. As a result, a great deal of data is generated. This complexity leads to insecure communication, traffic congestion, security, and privacy issues in vehicular networks (VNs). In addition, detecting malicious IVs, data integration, and data validation are major issues in VNs that affect network performance. A blockchain-based model for secure communication and malicious IV detection is proposed to address the above issues. In addition, this system also addresses data integration and transaction validation using an encryption scheme for secure communication. A multi-chain concept separates the legitimate and malicious data into two chains: the Integrity chain (I-chain) and Fraud chain (F-chain). This multi-chain mechanism solves the storage problem and reduces the computing power. The integration of blockchain in the proposed model provides privacy, network security, transparency, and immutability. To address the storage issue, the InterPlanetary File System (IPFS) is integrated with Certificate Authority (CA). A reputation mechanism is introduced to detect malicious IVs in the network based on ratings. This reputation mechanism is also used to prevent Sybil attack. The evaluation of the proposed work is based on the cost of smart contracts and computation time. Furthermore, two attacker models are presented to prevent the selfish mining attack and the Sybil attack. Finally, a security analysis of the proposed smart contracts with their security vulnerabilities is also presented.
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Affiliation(s)
- Tehreem Ashfaq
- Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
| | - Rabiya Khalid
- Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
| | - Adamu Sani Yahaya
- Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
- Department of Information Technology, Bayero University Kano, Kano 700006, Nigeria
| | - Sheraz Aslam
- Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, 3036 Limassol, Cyprus
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Automated Systems & Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 12435, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 52571, Saudi Arabia
| | - Mohamed Tounsi
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Automated Systems & Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 12435, Saudi Arabia
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Fati SM, Senan EM, Azar AT. Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases. Sensors (Basel) 2022; 22:s22114079. [PMID: 35684696 PMCID: PMC9185306 DOI: 10.3390/s22114079] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 05/27/2023]
Abstract
Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the survival of patients. Additionally, removing benign tumors in their early stages has more risks than benefits. Video endoscopy technology is essential for imaging the GI tract and identifying disorders such as bleeding, ulcers, polyps, and malignant tumors. Videography generates 5000 frames, which require extensive analysis and take a long time to follow all frames. Thus, artificial intelligence techniques, which have a higher ability to diagnose and assist physicians in making accurate diagnostic decisions, solve these challenges. In this study, many multi-methodologies were developed, where the work was divided into four proposed systems; each system has more than one diagnostic method. The first proposed system utilizes artificial neural networks (ANN) and feed-forward neural networks (FFNN) algorithms based on extracting hybrid features by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and fuzzy color histogram (FCH) algorithms. The second proposed system uses pre-trained CNN models which are the GoogLeNet and AlexNet based on the extraction of deep feature maps and their classification with high accuracy. The third proposed method uses hybrid techniques consisting of two blocks: the first block of CNN models (GoogLeNet and AlexNet) to extract feature maps; the second block is the support vector machine (SVM) algorithm for classifying deep feature maps. The fourth proposed system uses ANN and FFNN based on the hybrid features between CNN models (GoogLeNet and AlexNet) and LBP, GLCM and FCH algorithms. All the proposed systems achieved superior results in diagnosing endoscopic images for the early detection of lower gastrointestinal diseases. All systems produced promising results; the FFNN classifier based on the hybrid features extracted by GoogLeNet, LBP, GLCM and FCH achieved an accuracy of 99.3%, precision of 99.2%, sensitivity of 99%, specificity of 100%, and AUC of 99.87%.
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Affiliation(s)
- Suliman Mohamed Fati
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
| | - Ebrahim Mohammed Senan
- Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India;
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
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12
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Abbas SM, Javaid N, Azar AT, Qasim U, Khan ZA, Aslam S. Towards Enhancing the Robustness of Scale-Free IoT Networks by an Intelligent Rewiring Mechanism. Sensors (Basel) 2022; 22:2658. [PMID: 35408272 PMCID: PMC9003452 DOI: 10.3390/s22072658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/12/2022] [Accepted: 03/25/2022] [Indexed: 06/14/2023]
Abstract
The enhancement of Robustness (R) has gained significant importance in Scale-Free Networks (SFNs) over the past few years. SFNs are resilient to Random Attacks (RAs). However, these networks are prone to Malicious Attacks (MAs). This study aims to construct a robust network against MAs. An Intelligent Rewiring (INTR) mechanism is proposed to optimize the network R against MAs. In this mechanism, edge rewiring is performed between the high and low degree nodes to make a robust network. The Closeness Centrality (CC) measure is utilized to determine the central nodes in the network. Based on the measure, MAs are performed on nodes to damage the network. Therefore, the connections of the neighboring nodes in the network are greatly affected by removing the central nodes. To analyze the network connectivity against the removal of nodes, the performance of CC is found to be more efficient in terms of computational time as compared to Betweenness Centrality (BC) and Eigenvector Centrality (EC). In addition, the Recalculated High Degree based Link Attacks (RHDLA) and the High Degree based Link Attacks (HDLA) are performed to affect the network connectivity. Using the local information of SFN, these attacks damage the vital portion of the network. The INTR outperforms Simulated Annealing (SA) and ROSE in terms of R by 17.8% and 10.7%, respectively. During the rewiring mechanism, the distribution of nodes' degrees remains constant.
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Affiliation(s)
- Syed Minhal Abbas
- Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan; (S.M.A.); (N.J.)
| | - Nadeem Javaid
- Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan; (S.M.A.); (N.J.)
- School of Computer Science, University of Technology Sydney (UTS), Sydney, NSW 2007, Australia
| | - Ahmad Taher Azar
- Automated Systems & Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 12435, Saudi Arabia;
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt or
| | - Umar Qasim
- Department of Computer Science, University of Engineering and Technology Lahore (New Campus), Lahore 54000, Pakistan;
| | - Zahoor Ali Khan
- Computer Information Science, Higher Colleges of Technology, Fujairah 4114, United Arab Emirates;
| | - Sheraz Aslam
- Automated Systems & Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 12435, Saudi Arabia;
- Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
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13
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Sekhar GT, Pilla R, Azar AT, Dhananjaya M. Two-degree-of-freedom tilt integral derivative controller-based firefly optimisation for automatic generation control of restructured power system. IJCAT 2022. [DOI: 10.1504/ijcat.2022.126093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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14
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Azar AT, Serrano FE, Zhu Q, Bettayeb M, Fusco G, Na J, Zhang W, Kamal NA. Robust Stabilization and Synchronization of a Novel Chaotic System with Input Saturation Constraints. Entropy (Basel) 2021; 23:e23091110. [PMID: 34573735 PMCID: PMC8470018 DOI: 10.3390/e23091110] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/22/2021] [Accepted: 08/23/2021] [Indexed: 11/16/2022]
Abstract
In this paper, the robust stabilization and synchronization of a novel chaotic system are presented. First, a novel chaotic system is presented in which this system is realized by implementing a sigmoidal function to generate the chaotic behavior of this analyzed system. A bifurcation analysis is provided in which by varying three parameters of this chaotic system, the respective bifurcations plots are generated and evinced to analyze and verify when this system is in the stability region or in a chaotic regimen. Then, a robust controller is designed to drive the system variables from the chaotic regimen to stability so that these variables reach the equilibrium point in finite time. The robust controller is obtained by selecting an appropriate robust control Lyapunov function to obtain the resulting control law. For synchronization purposes, the novel chaotic system designed in this study is used as a drive and response system, considering that the error variable is implemented in a robust control Lyapunov function to drive this error variable to zero in finite time. In the control law design for stabilization and synchronization purposes, an extra state is provided to ensure that the saturated input sector condition must be mathematically tractable. A numerical experiment and simulation results are evinced, along with the respective discussion and conclusion.
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Affiliation(s)
- Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt
- Correspondence: or or
| | - Fernando E. Serrano
- Instituto de Investigación en Energía, Universidad Nacional Autonoma de Honduras (UNAH), Tegucigalpa 11101, Honduras; or
- Research Collaborator, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Quanmin Zhu
- FET–Engineering, Design and Mathematics, University of the West of England, Bristol BS16 1QY, UK;
| | - Maamar Bettayeb
- Electrical Engineering Department, University of Sharjah, Sharjah 27272, United Arab Emirates;
| | - Giuseppe Fusco
- Department of Electrical and Information Engineering, Universita degli Studi di Cassino e del Lazio Meridionale, 03043 Cassino, Italy;
| | - Jing Na
- Faculty of Mechanical & Electrical Engineering, Kunming University of Science and Technology, No. 727 Jingming South Road, Chenggong, Kunming 650500, China;
| | - Weicun Zhang
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;
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Fouad KM, Ismail MM, Azar AT, Arafa MM. Advanced methods for missing values imputation based on similarity learning. PeerJ Comput Sci 2021; 7:e619. [PMID: 34395861 PMCID: PMC8323724 DOI: 10.7717/peerj-cs.619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 06/09/2021] [Indexed: 06/13/2023]
Abstract
The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods' accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering algorithms. When records are not well-separated, as in the case of missing data, hard clustering provides a poor description tool in many cases. In general, the imputation depending on similar records is more accurate than the imputation depending on the entire dataset's records. Improving the similarity among records can result in improving the imputation performance. This paper proposes two numerical missing data imputation methods. A hybrid missing data imputation method is initially proposed, called KI, that incorporates k-nearest neighbors and iterative imputation algorithms. The best set of nearest neighbors for each missing record is discovered through the records similarity by using the k-nearest neighbors algorithm (kNN). To improve the similarity, a suitable k value is estimated automatically for the kNN. The iterative imputation method is then used to impute the missing values of the incomplete records by using the global correlation structure among the selected records. An enhanced hybrid missing data imputation method is then proposed, called FCKI, which is an extension of KI. It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation algorithms to impute the missing data in a dataset. The fuzzy c-means algorithm is selected because the records can belong to multiple clusters at the same time. This can lead to further improvement for similarity. FCKI searches a cluster, instead of the whole dataset, to find the best k-nearest neighbors. It applies two levels of similarity to achieve a higher imputation accuracy. The performance of the proposed imputation techniques is assessed by using fifteen datasets with variant missing ratios for three types of missing data; MCAR, MAR, MNAR. These different missing data types are generated in this work. The datasets with different sizes are used in this paper to validate the model. Therefore, proposed imputation techniques are compared with other missing data imputation methods by means of three measures; the root mean square error (RMSE), the normalized root mean square error (NRMSE), and the mean absolute error (MAE). The results show that the proposed methods achieve better imputation accuracy and require significantly less time than other missing data imputation methods.
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Affiliation(s)
- Khaled M. Fouad
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, Qaliobia, Egypt
- Faculty of Information Technology and Computer Science, Nile University, El Shikh Zaid, Giza, Egypt
| | - Mahmoud M. Ismail
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, Qaliobia, Egypt
| | - Ahmad Taher Azar
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, Qaliobia, Egypt
- College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - Mona M. Arafa
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, Qaliobia, Egypt
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Najm AA, Kasim Ibraheem I, Humaidi AJ, Azar AT. Output tracking and feedback stabilization for 6-DoF UAV using an enhanced active disturbance rejection control. IJIUS 2021. [DOI: 10.1108/ijius-09-2020-0059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
The hybrid control system of the nonlinear PID (NLPID) controller and improved active disturbance rejection control (IADRC) are proposed for stabilization purposes for a 6-degree freedom (DoF) quadrotor system with the existence of exogenous disturbances and system uncertainties.
Design/methodology/approach
IADRC units are designed for the altitude and attitude systems, while NLPID controllers are designed for the x−y position system on the quadrotor nonlinear model. The proposed controlling scheme is implemented using MATLAB/Simulink environment and is compared with the traditional PID controller and NLPID controller.
Findings
Different tests have been done, such as step reference tracking, hovering mode, trajectory tracking, exogenous disturbances and system uncertainties. The simulation results showed the demonstrated performance and stability gained by using the proposed scheme as compared with the other two controllers, even when the system was exposed to different disturbances and uncertainties.
Originality/value
The study proposes an NLPID-IADRC scheme to stabilize the motion of the quadrotor system while tracking a specified trajectory in the presence of exogenous disturbances and parameter uncertainties. The proposed multi-objective Output Performance Index (OPI) was used to obtain the optimum integrated time of the absolute error for each subsystem, UAV quadrotor system energy consumption and for minimizing the chattering phenomenon by adding the integrated time absolute of the control signals.
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17
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Abstract
This paper seeks to improve the efficiency of photovoltaic (PV) water pumping system using Fractional-order Fuzzy Maximum Power Point Tracking (FoF-MPPT) control and Gray Wolf Optimization (GWO) technique. The fractional calculus has been used to provide an enhanced model of PV water pumping system to, accurately, describe its nonlinear characteristics. Moreover, three metaheuristic optimizers are applied to tune the parameters of the proposed FoF-MPPT, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and the GWO. The FoF-MPPT is intensively tested and compared to the Perturb and Observe (PO), the Incremental Conductance (INC) and the FL-MPPT controllers. A MATLAB-Simscape based physical model of the PV water pumping system has been developed and simulated for different control techniques with the proposed optimization algorithms. The response of the PV water pumping systems is evaluated under rapidly changing weather conditions to prove the effectiveness of the optimized FoF-MPPT compared to the conventional algorithms. The reliability of the comparative study has been emphasized in terms of several transient tracking and steady- state performance indices under different operating conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FL-MPPT and FoF-MPPT control under different climatic conditions with disturbance rejection and robustness analysis.
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Affiliation(s)
- Raafat Shalaby
- Faculty of Electronic Engineering, Menofia University, Menouf, Egypt
- School of Engineering and Applied Science, Nile University, Sheikh Zayed City, Giza, Egypt
- Smart Engineering Systems Research Center (SESC), Nile University, Sheikh Zayed City, Giza, Egypt
| | - Hossam Hassan Ammar
- School of Engineering and Applied Science, Nile University, Sheikh Zayed City, Giza, Egypt
- Smart Engineering Systems Research Center (SESC), Nile University, Sheikh Zayed City, Giza, Egypt
| | - Ahmad Taher Azar
- Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
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18
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Pilla R, Gorripotu TS, Azar AT. Design and analysis of search group algorithm-based PD-PID controller plus redox flow battery for automatic generation control problem. IJCAT 2021. [DOI: 10.1504/ijcat.2021.119605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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19
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Pilla R, Azar AT, Gorripotu TS. Design and analysis of search group algorithm-based PD-PID controller plus redox flow battery for automatic generation control problem. IJCAT 2021. [DOI: 10.1504/ijcat.2021.10043119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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20
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Humaidi AJ, Ibraheem IK, Azar AT, Sadiq ME. A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles. Entropy (Basel) 2020; 22:e22070723. [PMID: 33286496 PMCID: PMC7517262 DOI: 10.3390/e22070723] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 06/22/2020] [Accepted: 06/23/2020] [Indexed: 11/26/2022]
Abstract
This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.
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Affiliation(s)
- Amjad J. Humaidi
- Control and Systems Engineering Department, University of Technology, Baghdad 10001, Iraq;
| | - Ibraheem Kasim Ibraheem
- Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad 10001, Iraq;
| | - Ahmad Taher Azar
- Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh 11586, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
- Correspondence: or
| | - Musaab E. Sadiq
- Ministry of Trade, General Company for Grain Processing, Baghdad 10001, Iraq;
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21
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Najm AA, Ibraheem IK, Azar AT, Humaidi AJ. Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System. Sensors (Basel) 2020; 20:s20123576. [PMID: 32599862 PMCID: PMC7349098 DOI: 10.3390/s20123576] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 06/16/2020] [Accepted: 06/17/2020] [Indexed: 11/16/2022]
Abstract
A consensus control law is proposed for a multi-agent system of quadrotors with leader–follower communication topology for three quadrotor agents. The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters. The complete nonlinear model is used without any further simplifications in the simulations, while simplification in the model is used to theoretically design the controller. Different case studies and tests are done (i.e., trajectory tracking formation and switching topology) to show the effectiveness of the proposed controller. The results show good performance in all tests while achieving the consensus of the desired formations.
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Affiliation(s)
- Aws Abdulsalam Najm
- Department of electrical engineering, College of engineering, University of Baghdad, Baghdad 10001, Iraq; (A.A.N.); (I.K.I.)
| | - Ibraheem Kasim Ibraheem
- Department of electrical engineering, College of engineering, University of Baghdad, Baghdad 10001, Iraq; (A.A.N.); (I.K.I.)
| | - Ahmad Taher Azar
- Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh 11586, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
- Correspondence:
| | - Amjad J. Humaidi
- Department of Control and Systems Engineering, University of Technology, Baghdad 10001, Iraq;
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22
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Ajeil FH, Ibraheem IK, Azar AT, Humaidi AJ. Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment. INT J ADV ROBOT SYST 2020. [DOI: 10.1177/1729881420929498] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.
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Affiliation(s)
- Fatin Hassan Ajeil
- Department of Electrical Engineering, College of Engineering, University of Baghdad, Al-Jadriyah, Baghdad, Iraq
| | - Ibraheem Kasim Ibraheem
- Department of Electrical Engineering, College of Engineering, University of Baghdad, Al-Jadriyah, Baghdad, Iraq
| | - Ahmad Taher Azar
- Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Amjad J Humaidi
- Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq
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Azar AT, Serrano FE, Rossell JM, Vaidyanathan S, Zhu Q. Adaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism. IJCAT 2020. [DOI: 10.1504/ijcat.2020.110404] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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25
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26
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Zhu Q, Flores MA, Vaidyanathan S, Serrano FE, Azar AT. Adaptive neural-fuzzy and backstepping controller for port-Hamiltonian systems. IJCAT 2020. [DOI: 10.1504/ijcat.2020.10025555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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27
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Fouad KM, Azar AT, Anter AM. Intelligent system for feature selection based on rough set and chaotic binary grey wolf optimisation. IJCAT 2020. [DOI: 10.1504/ijcat.2020.10030074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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28
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Azar AT, Serrano FE, Zhu Q, Vaidyanathan S, Rossell JM. Adaptive self-recurrent wavelet neural network and sliding mode controller/observer for a slider crank mechanism. IJCAT 2020. [DOI: 10.1504/ijcat.2020.10032593] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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29
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Mohamed II, Aboamer MA, Azar AT, Wahba K, Schumann A, Bär KJ. Nonlinear single-input single-output model-based estimation of cardiac output for normal and depressed cases. Neural Comput Appl 2019. [DOI: 10.1007/s00521-017-3245-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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30
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Babajani R, Abbasi M, Azar AT, Bastan M, Yazdanparast R, Hamid M. Integrated safety and economic factors in a sand mine industry: a multivariate algorithm. IJCAT 2019. [DOI: 10.1504/ijcat.2019.101180] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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31
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Azar AT, Hassan H, Razali MSAB, de Brito Silva G, Ali HR. Two-Degree of Freedom Proportional Integral Derivative (2-DOF PID) Controller for Robotic Infusion Stand. Advances in Intelligent Systems and Computing 2019:13-25. [DOI: 10.1007/978-3-319-99010-1_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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32
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Radwan AG, Emira AA, AbdelAty AM, Azar AT. Modeling and analysis of fractional order DC-DC converter. ISA Trans 2018; 82:184-199. [PMID: 28709651 DOI: 10.1016/j.isatra.2017.06.024] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 03/08/2017] [Accepted: 06/26/2017] [Indexed: 06/07/2023]
Abstract
Due to the non-idealities of commercial inductors, the demand for a better model that accurately describe their dynamic response is elevated. So, the fractional order models of Buck, Boost and Buck-Boost DC-DC converters are presented in this paper. The detailed analysis is made for the two most common modes of converter operation: Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). Closed form time domain expressions are derived for inductor currents, voltage gain, average current, conduction time and power efficiency where the effect of the fractional order inductor is found to be strongly present. For example, the peak inductor current at steady state increases with decreasing the inductor order. Advanced Design Systems (ADS) circuit simulations are used to verify the derived formulas, where the fractional order inductor is simulated using Valsa Constant Phase Element (CPE) approximation and Generalized Impedance Converter (GIC). Different simulation results are introduced with good matching to the theoretical formulas for the three DC-DC converter topologies under different fractional orders. A comprehensive comparison with the recently published literature is presented to show the advantages and disadvantages of each approach.
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Affiliation(s)
- Ahmed G Radwan
- Engineering Mathematics and Physics Dept, Faculty of Engineering, Cairo University, Giza, Egypt; Nanoelectronics Integrated Systems Center (NISC), Nile University, Giza, Egypt.
| | - Ahmed A Emira
- Electronics and Communications Department, Cairo University, Giza, Egypt
| | - Amr M AbdelAty
- Engineering Mathematics and Physics Dept, Faculty of Engineering, Fayoum University, Egypt
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Jothi G, Inbarani HH, Azar AT, Devi KR. Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3359-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Djouima M, Azar AT, Drid S, Mehdi D. Higher Order Sliding Mode Control for Blood Glucose Regulation of Type 1 Diabetic Patients. International Journal of System Dynamics Applications 2018. [DOI: 10.4018/ijsda.2018010104] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Type 1 diabetes mellitus (T1DM) treatment depends on the delivery of exogenous insulin to obtain near normal glucose levels. This article proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on comparing the first order sliding mode control (FOSMC) with a higher order SMC based on the super twisting control algorithm. The higher order sliding mode is used to overcome chattering, which can induce some undesirable and harmful phenomena for human health. In order to test the controller in silico experiments, Bergman's minimal model is used for studying the dynamic behavior of the glucose and insulin inside human body. Simulation results are presented to validate the effectiveness and the good performance of this control technique. The obtained results clearly reveal improved performance of the proposed higher order SMC in regulating the blood glucose level within the normal glycemic range in terms of accuracy and robustness.
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Affiliation(s)
- Mounir Djouima
- Electronics Department, LEA, University of Batna 2, Mostafa Benboulaid, Batna, Algeria
| | - Ahmad Taher Azar
- Faculty of Computers and Information, Benha University, Benha, Egypt & School of Engineering and Applied Sciences, Nile University, Giza, Egypt
| | - Saïd Drid
- LSP-IE, University of Batna 2, Batna, Mostafa Benboulaid, Algeria
| | - Driss Mehdi
- University of Poitiers, Poitiers Cedex, France
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Bastan M, Hamid M, Ghazizadeh A, Azar AT, Mahlooji H. Single-step change point estimation in nonlinear profiles using maximum likelihood estimation. IJIEI 2018. [DOI: 10.1504/ijiei.2018.10017813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Ghazizadeh A, Mahlooji H, Azar AT, Hamid M, Bastan M. Single-step change point estimation in nonlinear profiles using maximum likelihood estimation. IJIEI 2018. [DOI: 10.1504/ijiei.2018.096570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Moysis L, Azar AT, Kafetzis I, Tsiaousis M, Charalampidis N. Introduction to Control Systems Design Using Matlab. International Journal of System Dynamics Applications 2017. [DOI: 10.4018/ijsda.2017070107] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Control systems theory is a wide area covering a range of artificial and physical phenomena. Control systems are systems that are designed to operate under strict specifications, to satisfy certain aims, like safety regulations in the industry, optimal production of goods, disturbance rejection in vehicles, smooth movement and placement of objects in warehousing, regulation of drug administration in medical operations, level control in chemical processes and many more. The present work provides an introduction to the fundamental principles of control system's analysis and design through the programming environment of Matlab and Simulink. Analysis of transfer function models is carried out though multiple examples in Matlab and Simulink, analyzing the dynamics of 1st and 2nd order systems, the role of the poles and zeros in the system's dynamic response, the effects of delay and the possibility to approximate higher order systems by lower order ones. In addition, examples are given from the fields of mechanical systems, medically induced anesthesia, neuroprosthetics and water level control, showcasing the use of controllers that satisfy certain design specifications.
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Affiliation(s)
- Lazaros Moysis
- School of Mathematical Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece,
| | - Ahmad Taher Azar
- Faculty of Computers and Information, Benha University, Benha, Egypt
| | - Ioannis Kafetzis
- School of Mathematical Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Michail Tsiaousis
- School of Mathematical Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikolaos Charalampidis
- School of Mathematical Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Azar AT, Serrano FE. Stabilization of Mechanical Systems with Backlash by PI Loop Shaping. ARTIF INTELL 2017. [DOI: 10.4018/978-1-5225-1759-7.ch097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Azar AT, Serrano FE. Stabilization and Control of Mechanical Systems with Backlash. ARTIF INTELL 2017. [DOI: 10.4018/978-1-5225-1759-7.ch057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Ouannas A, Azar AT, Vaidyanathan S. New hybrid synchronisation schemes based on coexistence of various types of synchronisation between master-slave hyperchaotic systems. IJCAT 2017. [DOI: 10.1504/ijcat.2017.082868] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
Chaotic behavior is a term that is attributed to dynamical systems whose solutions are highly sensitive to initial conditions. This means that small perturbations in the initial conditions can lead to completely different trajectories in the solution space. These types of chaotic dynamical systems arise in various natural or artificial systems in biology, meteorology, economics, electrical circuits, engineering, computer science and more. Of these innumerable chaotic systems, perhaps the most interesting are those that exhibit attracting behavior. By that, the authors refer to systems whose trajectories converge with time to a set of values, called an attractor. This can be a single point, a curve or a manifold. The attractor is called strange if it is a set with fractal structure. Such systems can be both continuous and discrete. This paper reports on some new chaotic discrete time two dimensional maps that are derived from simple modifications to the well-known Hénon, Lozi, Sine-sine and Tinkerbell maps. Numerical simulations are carried out for different parameter values and initial conditions and it is shown that the mappings either diverge to infinity or converge to attractors of many different shapes.
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Affiliation(s)
- Lazaros Moysis
- School of Mathematical Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece,
| | - Ahmad Taher Azar
- Faculty of Computers and Information, Benha University, Benha, Egypt
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Vaidyanathan S, Ouannas A, Azar AT. New hybrid synchronisation schemes based on coexistence of various types of synchronisation between master-slave hyperchaotic systems. IJCAT 2017. [DOI: 10.1504/ijcat.2017.10003542] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Abstract
Backlash is one of several discontinuities found in different kinds of systems, it can be found in actuators of different types, such as mechanical and hydraulic, giving way to unwanted effects in the system behavior. PI loop shaping control design implementing a describing function to find the limit cycle oscillations and the appropriate control gain is developed. Therefore a frequency domain approach is implemented for the control of nonlinear system of any kind such as robotics, mechatronics, other kind of mechanisms, electrical motors etc. Finally, in order to corroborate the theoretical background explained in this article, the stabilization of a cart-pendulum system with the proposed control strategy is shown.
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Affiliation(s)
- Ahmad Taher Azar
- Faculty of Computers and Information, Benha University, Banha, Egypt,
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Azar AT, Inbarani HH, Kumar SU, Own HS. Hybrid system based on bijective soft and neural network for Egyptian neonatal jaundice diagnosis. IJIEI 2016. [DOI: 10.1504/ijiei.2016.074506] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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