Implementation of Bio-inspired Algorithms in Designing Optimized PID controller for Cuk Converter for Enhanced Performance: A Software based Approach
Keywords:Cuk Converter, PID Controller, Bio-inspired Algorithm, Firefly Algorithm, Particle Swarm Optimization, Artificial Bee Colony
This paper represents the idea of implementing bio-inspired algorithms in designing an optimized PID controller to investigate the stability and improve the performance of the closed-loop CUK converter. Bio-inspired algorithms (BIA) such as Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and, Artificial Bee Colony (ABC) are stochastic optimization techniques and have been increasingly applied to attain optimal solution for designing and optimizing power converters in current times. The closed-loop transfer function of the CUK converter has been developed by the State Space Average (SSA) technique. This paper assesses both the cases of converter stability when it is integrated with the conventional PID controller and the BIA-PID controllers (FA-PID, PSO-PID, ABC-PID) and eventually compares the outcomes from all the controllers. For examining the stability of the system, three objective functions (IAE, ITAE, and ISE) and various performance specifications such as percentage of overshoot, rise time, settling time, and peak amplitude are tabulated. MATLAB and Simulink are used to carry out the simulations meticulously. Hence, a comparative analysis is illustrated in this paper to state a clear-sighted evaluation of the performances.
X. Fan, W. Sayers, S. Zhang, Z. Han, L. Ren, H. Chizari: Review and classification of bio-inspired algorithms and their applications. Journal of Bionic Engineering, 17, (2020), 611-631.
A. Isaac, H. K. Nehemiah, A. Isaac, A. Kannan: Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms. Computers in Biology and Medicine, Elsevier, 124, (2020).
M. M. Nishat, F. Faisal, M. A. Hoque, “Modeling and Stability Analysis of a DC-DC SEPIC Converter by Employing Optimized PID Controller Using Genetic Algorithm,” International Journal of Electrical & Computer Sciences, 19(01), (2019), 1-7.
P. S. Nayak, T. A. Rufzal: Performance analysis of feedback controller design for induction motor soft-starting using bio-inspired algorithms. 2018 International Conference on Power, Instrumentation, Control and Computing (PICC), (2018).
G. Beni: Swarm intelligence. Complex Social and Behavioral Systems: Game Theory and Agent-Based Models, Springer, (2020).
K. Hussain, M. N. Salleh, S. Cheng, Y. Shi: On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Computing and Applications, 31(11), (2019), 7665-7683.
Ata Jahangir Moshayedi, Jinsong Li, Nima Sina, Xi Chen, Liefa Liao, Mehdi Gheisari, Xiaoyun Xie, "Simulation and Validation of Optimized PID Controller in AGV (Automated Guided Vehicles) Model Using PSO and BAS Algorithms", Computational Intelligence and Neuroscience, vol. 2022, Article ID 7799654, 22 pages, 2022. https://doi.org/10.1155/2022/7799654
A. J. Moshayedi, A. Shuvam Roy, S. K. Sambo, Y., Zhong, and L. Liao, “Review On: The Service Robot Mathematical Model”, EAI Endorsed Trans AI Robotics, vol. 1, p. e8, Feb. 2022
A. J. Moshayedi, A. Abbasi, L. Liao and S. Li, "Path planning and trajectory tracking of a mobile robot using bio-inspired optimization algorithms and PID control," CIVEMSA, 2019, pp. 1-6, doi: 10.1109/CIVEMSA45640.2019.9071596.
D. Pathak, G. Sagar, P. Gaur: An Application of Intelligent Non-linear Discrete-PID Controller for MPPT of PV System. Procedia Computer Science, 167(16), (2020), 1574-1583.
M. M. Nishat, F. Faisal, M. A. M. Oninda and M. A. Hoque, "Modeling Simulation and Performance Analysis of SEPIC Converter Using Hysteresis Current Control and PI Control Method", 2018 Int. Conf. on Inv. in Science Engg. and Tech. (ICISET), pp. 7-12
M. M. Nishat, F. Faisal, M. Rahman and M. A. Hoque, "Modeling and Design of a Fuzzy Logic Based PID Controller for DC Motor Speed Control in Different Loading Condition for Enhanced Performance", 1st Int. Conf. on Advances in Science Engg. and Robotics Tech. (ICASERT), pp. 1-6, 2019.
A. A. Mehadi, M. M. Nishat, F. Faisal, A. R. H. Bhuiyan, M. Hussain and M. A. Hoque, "Design Simulation and Feasibility Analysis of Bifacial Solar PV System in Marine Drive Road Cox's Bazar", International Conference on Science & Cont. Technology (ICSCT), pp. 1-6, 2021.
A. A. Mehadi, M. A. Chowdhury, M. M. Nishat, F. Faisal and M. M. Islam, "A software-based approach in designing a rooftop bifacial PV system for the North Hall of Residence IUT", Clean Energy, vol. 5, no. 3, pp. 403-422, 2021.
F. Faisal, M. M. Nishat and M. R. Mia, "An Investigation on DC Motor Braking System by Implementing Electromagnetic Relay and Timer", International Conference on Electrical Computer and Communication Engineering, pp. 1-6, 2019.
M. M. Nishat, M. R. K. Shagor, H. Akter, S. A. Mim, F. Faisal, M. M. Nishat, et al., "An Optimal Design of PID controller for DC-DC Zeta converter using Particle Swarm Optimization", 23rd International Conference on Comp. and Info. Tech. (ICCIT), pp. 1-6, 2020.
M. D. Rahman, F. Faisal, M. M. Nishat and M. R. K. Shagor, " Design and Analysis of Passive LC 3 Component Boost Converter ", 2021 IEEE Madras Section Conference (MASCON), pp. 1-6, 2021.
M. R. K. Shagor, A. J. Mahmud, M. M. Nishat, F. Faisal, M. H. Mithun and M. A. Khan, "Firefly Algorithm Based Optimized PID Controller for Stability Analysis of DC-DC SEPIC Converter", 12th UEMCON, pp. 0957-0963, 2021
A. A. Mehadi et al., "Design simulation and analysis of monofacial solar pv panel based energy system for university residence: a case study", IOP Conf. Series: Mater. Sci. and Eng., vol. 1045, no. 1, pp. 012011, 2021.
F. Faisal, M. Rahman and F. B. Hashem, “Speed control of dc motor using Self-tuned fuzzy PID controller”, Diss. Department of EEE, IUT, OIC, Gazipur-1704, Bangladesh, 2015.
F. Faisal and M. M. Nishat, "An Investigation for Enhancing Registration Performance with Brain Atlas by Novel Image Inpainting Technique using Dice and Jaccard Score on Multiple Sclerosis (MS) Tissue," Biomed. and Pharm. J., vol. 12, no. 3, pp. 1249-1262, 2019
M. M. Nishat, et al., "An Investigative Approach to Employ Support Vector Classifier as a Potential Detector of Brain Cancer from MRI Dataset," ICECIT, pp. 1-4, doi: 10.1109/ICECIT54077.2021.9641168
M. A. A. R. Asif et al., "Performance Evaluation and Comparative Analysis of Different ML Algorithms in Predicting Cardiovascular Disease," Engineering Letters, vol. 29, no. 2, pp. 731-741, 2021
M. M. Nishat et al., “A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms,” EAI Endorsed Transactions on Pervasive Health and Technology, vol. 18, no. e6, 2021, doi: 10.4108/eai.13-8-2021.170671.
M. R. Farazi, F. Faisal, Z. Zaman and S. Farhan, "Inpainting multiple sclerosis lesions for improving registration performance with brain atlas," MediTec, pp 1-6, doi: 10.1109/MEDITEC.2016.7835363.
M. A. A. R. Asif et al., "Computer Aided Diagnosis of Thyroid Disease Using Machine Learning Algorithms," ICECE, 2020, pp. 222-225, doi: 10.1109/ICECE51571.2020.9393054
M. M. Nishat and F. Faisal, "An Investigation of Spectroscopic Characterization on Biological Tissue," iCEEiCT, 2018, pp. 290-295, doi: 10.1109/CEEICT.2018.8628081
F. Faisal, et al., "Covid-19 and its impact on school closures: a predictive analysis using machine learning algorithms," ICSCT, 2021, pp. 1-6, doi: 10.1109/ICSCT53883.2021.9642617
A. A. Rahman et al., "Detection of Epileptic Seizure from EEG Signal Data by Employing Machine Learning Algorithms with Hyperparameter Optimization," BioSMART, 2021, pp. 1-4, doi: 10.1109/BioSMART54244.2021.9677770.
M. M. Nishat, et al., "Detection of Parkinson's Disease by Employing Boosting Algorithms," ICIEV and icIVPR, pp. 1-7, 2021, doi: 10.1109/ICIEVicIVPR52578.2021.956410.
Nishat, M. M., et al. “Detection of Autism Spectrum Disorder by Discriminant Analysis Algorithm,” BIM, pp. 473-482, Springer, Singapore, 2022 doi: 10.1007/978-981-16-6636-0_36
A. A. Rahman, et al., "Detection of Mental State from EEG Signal Data: An Investigation with Machine Learning Classifiers," KST, pp. 152-156, 2022, doi: 10.1109/KST53302.2022.9729084.
M. R. Rahman, S. Tabassum, E. Haque, M. M. Nishat, F. Faisal and E. Hossain, "CNN-based Deep Learning Approach for Microcrack Detection of Solar Panels," 2021 3rdInt. Conf. on STI 4.0, 2021, pp. 1-6, doi: 10.1109/STI53101.2021.9732592.
Nishat, M. M., et al. “A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset,” Scientific Programming, https://doi.org/10.1155/2022/3649406.
Nishat, M. M, Faisal, F., Mahbub, M. A., Mahbub, M. H., et al., “Performance Assessment of Different Machine Learning Algorithms in Predicting Diabetes Mellitus”. Biosc.Biotech.Res.Comm.,14(1), 2021 doi: http://dx.doi.org/10.21786/bbrc/14.1/10.
F. Faisal, “Image Inpainting to Improve the Registration Performance of Multiple Sclerosis (MS) Patient Brain with Brain Atlas,” Diss. Department of EEE, IUT, OIC, Gazipur-1704, Bangladesh, 2016.
M. M. Nishat et al., "Performance Investigation of Different Boosting Algorithms in Predicting Chronic Kidney Disease," 2020 2nd Int. Conf. on STI 4.0, pp. 1-5, 2020, doi: 10.1109/STI50764.2020.9350440
M. Islam, M. Tabassum, M. M. Nishat, F. Faisal and M. S. Hasan, "Real-Time Clinical Gait Analysis and Foot Anomalies Detection Using Pressure Sensors and Convolutional Neural Network," ICBIR, 2022, pp. 717-722, doi: 10.1109/ICBIR54589.2022.9786472
I. J. Ratul, A. A. Monsur, B. Tabassum, A. M. Ar-Rafi, M. M. Nishat and F. Faisal, “Early risk prediction of cervical cancer: A machine learning approach,” ECTI-CON, 2022, pp. 1-4, doi: 10.1109/ECTI-CON54298.2022.9795429.
A. Al–Monsur, M. R. Kabir, A. M. Ar–Rafi, M. M. Nishat and F. Faisal, "Covid-EnsembleNet: An Ensemble Based Approach for Detecting Covid-19 by utilising Chest X-ray Images," AIIoT, 2022, pp. 351-356, doi: 10.1109/AIIoT54504.2022.9817237.
I. A. Jahan, et al., "Accident Detection and Road Condition Monitoring Using Blackbox Module," ICUFN, 2022, pp. 247-252, doi: 10.1109/ICUFN55119.2022.9829589.
A. A. Rahman et al., “Perceived Stress Analysis of Undergraduate Students during COVID-19: A ML Approach,” MELECON, 2022, pp. 1129-1134, doi: 10.1109/MELECON53508.2022.9843081.
F. Faisal, M. M. Nishat and M. A. M. Oninda, "Spectroscopic Characterization of Biological Tissue using Quantitative Acoustics Technique," iCEEiCT, doi: 10.1109/CEEICT.2018.8628146
Z. K. Eisham et al., “Chimp optimization algorithm in multilevel image thresholding and image clustering,” Evolving Systems, 2022. https://doi.org/10.1007/s12530-022-09443-3.
F. Faisal, M. A. Salam, M. B. Habib, M. S. Islam and M. M. Nishat, “Depth estmation from Video using Computer Vision and Machine Learning with Hyperparameter Optimization,” ICSSA, 2022, pp. 39-44, doi: 10.1109/ICSSA54161.2022.9870961.
S. Hassan et al., “Comparative Analysis of Machine Learning Algorithms in Detection of Brain Tumor,” IBDAP, 2022, pp. 31-36, doi: 10.1109/IBDAP55587.2022.9907433. IEEE, 2022
I. J. Ratul et al., “Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different MLCs by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospe. Analysis”, CMMM, https://doi.org/10.1155/2022/9391136.
T. Hasan, et al., “Employment of Ensemble Machine Learning Methods for Human Activity Recognition”, Journal of Healthcare Engineering, vol. 2022, https://doi.org/10.1155/2022/6963891.
M. R. Kabir, et al., “Procuring MFCCs from Crema-D Dataset for Sentiment Analysis using DL Models with Hyperparameter Tuning,” RAAICON, pp. 50-55. doi: 10.1109/RAAICON54709.2021.9929975.
M. K. Mahadi et al., “Machine Learning Assisted Decision Support System for Prediction of Prostate Cancer,” ECTI-CON, 2023, pp. 1-5, doi: 10.1109/ECTI-CON58255.2023.10153167.
A. A. Rahman, M. R. Kabir, R. H. Ratul, F. A. Shams, M. M. Nishat and F. Faisal, “An Efficient Analysis of EEG Signals to Perform Emotion Analysis”, 4th Int. Conf. on Artificial Intelligence, Robotics and Control (AIRC 2023), Cairo, Egypt, IEEE, accepted, in press
F. Faisal, M. M. Nishat, K. R. Raihan, A. Shafiullah and S. Ali, “A Machine Learning Approach for Analyzing and Predicting Suicidal Thoughts and Behaviors,” ICUFN, 2023, pp. 43-48, doi: 10.1109/ICUFN57995.2023.10201075.
F. A. Shams, R. H. Ratul, A. I. Naf, S. S. H. Samir, M. M. Nishat, F. Faisal and M. A. Hoque, “Investigation on Machine Learning Based Approaches for Estimating the Critical Temperature of Superconductors”, AIRoSIP, IEEE 2023, accepted, in press
I. J. Ratul, M. M. Nishat, F. Faisal, S. Sultana, A. Ahmed, M A. A. Mamun, “Analyzing Perceived Psychological and Social Stress of University Students: A Machine Learning Approach”, Heliyon, 9(6), E17207, doi: 10.1016/j.heliyon.2023.e17307.
M. N. S. Shahi, et al., "Automated Vehicle Access and Exit Control: A Smart Parking Management System using Finite State Machine in VHDL," RI2C, doi: 10.1109/RI2C56397.2022.9910305.
F. Faisal, et al., "Ex-Vivo Design of pH meter To Analyze Nutrients Feature Assessment for Economic Domestic Purpose," ITC-CSCC, 2022, pp. 637-640, doi: 10.1109/ITC-CSCC55581.2022.9895032.
A. Shafiullah, F. Faisal, R. H. Badhon, S. Ali, M. M. Nishat, M. R. Muttaqi and M. A. Billah, “Detection of Skin Cancer: A Deep Learning Approach,” 20th Int. Conf. on Ubiquitous Intelligence and Computing (UIC 2023), Portsmouth, UK, IEEE, accepted, in press
N. Sakib, Z. B. Saif, M. T. Hasan, M. Adnan, F. Faisal, M. M. Nishat, et al., “EEG-Driven Age Prediction: Advancements in Machine Learning Models”, ICE3IS, IEEE 2023, accepted, in press.
Z. B. Saif, N. Sakib, M. Adnan, M. T. Hasan, M. M. Nishat, F. Faisal, et al., “Sensorimotor Activity Patterns using Machine Learning: Assessing the Impact of Auditory Timing Perception and Comparing Different Algorithms”, ICRAIE, IEEE 2023, accepted, in press
C. Olivares-Rodríguez, T. Castillo-Calzadilla, O. Kamara-Esteban: Bio-inspired approximation to MPPT under real irradiation conditions. International Symposium on Intelligent and Distributed Computing, pp. 107-118, Springer, Cham, (2018).
M. M. Nishat, F. Faisal, A. J. Evan, M. M. Rahaman, M. S. Sifat, H. F. Rabbi: Development of Genetic Algorithm (GA) Based Optimized PID Controller for Stability Analysis of DC-DC Buck Converter. Journal of Power and Energy Engineering, 8(9), (2020), 8-19.
S. Abdelmalek, A. Dali, M. Bettayeb, A. Bakdi: A new effective robust nonlinear controller based on PSO for interleaved DC–DC boost converters for fuel cell voltage regulation. Soft Computing, 24(22), (2020), 17051-17064.
D. Kumar, B.R. Gandhi, R.K. Bhattacharjya: Firefly Algorithm and Its Applications in Engineering Optimization. Nature-Inspired Methods for Metaheuristics Optimization, pp. 93-103. Springer, Cham, 2020.
G. F. Gomes, J.V.P. Pereira: Sensor placement optimization and damage identification in a fuselage structure using inverse modal problem and firefly algorithm. Evolutionary Intelligence, 13(4), (2020), 571-591.
M. A. Saleh, M. Soliman, H. H. Ammar, M. A. Shalaby: Modeling and control of 3-omni wheel Robot using PSO optimization and Neural Network. International Conference on Control, Automation and Diagnosis (ICCAD), (2020).
G. Marimuthu, M. G. Umamaheswari: Analysis and Design of Single Stage Bridgeless Cuk Converter for Current Harmonics Suppression Using Particle Swarm Optimization Technique. Electric Power Components and Systems, 47(11-12), (2019), 1101-1115.
N. Paliwal, L. Srivastava, M. Pandit: PSO-based PID controller designing for LFC of single area electrical power network. Nature Inspired Optimization for Electrical Power System, pp. 43-54. Springer, Singapore, 2020.
V. B. Kumar, G. Charan, Y. V. P. Kumar: Design of robust PID controller for improving voltage response of a cuk converter. Innovations in Electrical and Electronic Engineering, pp. 301-318. Springer, Singapore, 2021.
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