Amelioration of ship control with improved dynamic response of motor controller

• Autorzy: Hasan Shameem , Sarowar Golam

Identyfikatory

DOI

10.15199/48.2021.04.06

Warianty tytułu

PL

Poprawa możliwości sterowanie silnikiem statku z uwzględnieniem odpowiedzi dynamicznej

• Języki publikacji: EN

Abstrakty

EN

During a sea voyage, it is significantly important to fix the speed and angle of movement of the ship precisely depending on the distance of the destination and also in icebreaking purpose. So, enhancement of transient response of commutator motor used in the ship is essential. Due to their better reliabilities as well as low costing, nowadays, Direct Current (commutator) motors are utilized in icebreaking ships, in industrial near and far applications, robotic manipulators, and also for home appliances. Thus, it is vital to introduce a suitable controller for managing the speed and transient behavior of a Direct Current. In this study, for the enhancement of dynamic response, various DC commutator motor controllers have been simulated. At first, the DC commutator motor parameters are selected and four optimized controllers: Ziegler-Nichols (ZN)-based conventional Proportional Integral Derivative (PID) controller, Genetic Algorithm (GA)-based PID controller (GA-PID), as well as Flower Pollination Algorithm (FPA)-based PID (FPA-PID) are designed and simulated to manage the angular speed together with dynamic response of shaft of a Direct Current commutator motor actuator. The electric actuator response for every controller is ascertained as well as compared after applying step input that is necessary for the simulation of transient response of the motor. The performance analysis shows that the FPA-PID controller is adequate for the steering task in the ship, which requires precision and associated with transient response properties.

PL

W artykule analizowano różne metody sterowania silnikiem komutacyjnym DC. Szczególną uwagę poświęcono sterownikom PID , PID wspomaganym algorytmami genetycznymi oraz Flower Pollination FPA-PID. . Analizowano możliwości sterowania prędkością przy odpowiednich parametrach dynamicznych. (odpowiedź na wymuszenie skokowe). Stwierdzono, że najlepsze właściwości do sterowani silnikiem statku ma algorytm FPA-PID.

Słowa kluczowe

EN

flower pollination algorithm; genetic algorithm; proportional-integral-derivative; transient response

PL

sterowanie silnikiem komutatorowym; silnik statku; algorytm FPA-PID; odpowiedź dynamiczna

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2021
• Tom: R. 97, nr 4
• Strony: 32--40
• Opis fizyczny: Bibliogr. 42 poz., rys., tab.

Twórcy

autor

Hasan Shameem

• University of Liberal Arts Bangladesh (ULAB)House - 56, Road - 4/A, Satmasjid Road, Dhaka 1209, Bangladesh

autor

Sarowar Golam

• Islamic University of Technology (IUT), Boardbazar, Gazipur - 1704, Bangladesh

Bibliografia

• [1] A. Andrzejewski “Time-Optimal Position Control of DC Motor Servo Drive” Przegląd Elektrotechniczny, 12/2019, R. 95, pp. 85-88.
• [2] K. Ogata K., “Modern Control Engineering”, 4th Edition, 2015, Dorling Kindersley Pvt. Ltd., India.
• [3] R. C. Dorf, R.C. and R.H. Bishop, “Modern Control Systems”, 9th edition, Prentice-Hall, Inc., Upper Saddle River, NJ, 2001.
• [4] Manafeddin Namazov-Onur Basturk, “DC motor position control using fuzzy proportional-derivative controllers with different defuzzification methods”, Turkish Journal of Fuzzy Systems, Vol. 1 (2010), No.1, pp. 36-54.
• [5] N. S. Nise, “Control System Engineering”, John Wiley and Sons Inc., New York, 2011.
• [6] Md. M. Gani, Md. S. Isalm, Muhammad A. Ullah, “Modeling and Designing a Genetically Optimized PID Controller for Separately Excited DC Motor,” International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019.
• [7] B. Kada, A. Elkebir, K. Negadi, Hafida Belhadj and D. E. Chaouch “Application of Adaptive Controller Neural Network Based on RBF NN for Temperature Control Electrical Resistance Furnace”, Przegląd Elektrotechniczny, 6/2020, R. 96, pp. 33-38.
• [8] J. G. Ziegler and N. B. Nichols, “Optimum Settings for Automatic Controllers”, Transactions on ASME, Vol. 64 (1942), No. 11, pp. 759–768.
• [9] D. Karaboga, B. Gorkemli, C. Ozturk and N. Karaboga “A comprehensive survey: artificial bee colony (ABC) algorithm and applications”, Artificial Intelligence review, Springer, vol. 42 (2014), pp. 21-57.
• [10] J. M. Herrero, X. Blasco, M. Martinez and J. V. Salcedo, “Optimal PID Tuning with Genetic Algorithm for Non Linear Process Models.” 15th Triennial World Congress, Elsevier, pp. 31-36, 2002.
• [11] J. Kennedy, “Particle swarm optimization”, Encyclopedia of Machine Learning, Springer, pp. 760–766, 2011.
• [12] S. Mirjalili, S. M. Mirjalili, A. Lewis, “Grey wolf optimizer”, Advances in Engineering Software, vol. 69 (2014), pp. 46-61.
• [13] S. Arora, H. Singh, M. Sharma, S. K. Sharma, P. Anand, “A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection,” IEEE Access, Vol. 7 (2019), pp. 26343-26361.
• [14] S. Kalra and S. Arora, “Firefly algorithm hybridized with flower pollination algorithm for multimodal functions.” Proceedings of the International Congress on Information and Communication Technology, Springer, pp. 207–219, 2016.
• [15] X. S. Yang, “A new metaheuristic bat-inspired algorithm”, Nature Inspired Cooperative Strategies for Optimization, Springer, pp. 65–74, 2010.
• [16] A. H. Gandomi, X. S. Yang and A. H. Alavi, “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems”, Engineering with Computers, Vol. 29 (2013), No. 1, pp. 17–35.
• [17] X. S. Yang, “Flower pollination algorithm for global optimization”, International Conference on Unconventional Computing and Natural Computation, Springer, pp. 240–249, 2012.
• [18] A. Y. Al-Maliki and K. Iqbal, “PID-Type FLC Controller Design and Tuning for Sensorless Speed Control of DC Motor”, Advances in Science, Technology and Engineering Systems Journal, Vol. 3 (2018), No. 6, pp. 515-522.
• [19] Wahyudi, M. Rosalina, A. Ajulian, B. Winardi, “Self-Tuning Fuzzy PID Design for BLDC Speed Control” GRD Journals – Global Research and Development Journal for Engineering, Vol. 3 (2018), pp. 4-11. 40 PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 97 NR 4/2021
• [20] J. Pongfai and W. Assawinchaichote, “Self-tuning PID parameters using NN-GA for Brush DC motor control system” 14th International IEEE Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 111-114, 2017.
• [21] H. S. Hameed, “Brushless DC Motor Controller Design Using Matlab Applications”, 1st International Scientific Conference of Engineering Sciences – 3rd Scientific Conference of Engineering Science (ISCES), pp. 44-49, 2018.
• [22] H. Chaudhary, S. Khatoon and R. Singh, “ANFIS Based Speed Control of DC Motor”, IEEE conference on Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity, pp. 63-67. 2016.
• [23] M. K. Rout, D. Sain, S. K. Swain and S. K. Mishra, “PID Controller Design for Cruise Control System using Genetic Algorithm”, IEEE International Conference on Electrical, Electronics, and Optimization Techniques, pp. 4170 - 4175, 2016.
• [24] Z. Has, A. H. Muslim and N. A. Mardiyah, “Adaptive-Fuzzy-PID Controller Based Disturbance Observer for DC Motor Speed Control”, Proceedings of IEECSI, 2017.
• [25] N. P. Adhikari, M. Choubey and R. Singh, “DC Motor Control Using Ziegler Nichols and Genetic Algorithm Technique”, International Journal of Electrical, Electronics and Computer Engineering, Vol. 1 (2012), No. 1, pp. 33-36.
• [26] P. M. Meshram and R. G. Kanojiya, “Tuning of PID Controller using Ziegler-Nichols Method for Speed Control of DC Motor”, IEEE- International Conference On Advances in Engineering, Science and Management, pp. 117-122, 2012.
• [27] Y. A. Almatheel and A. Abdelrahman, “Speed Control of DC Motor Using Fuzzy Logic Controller”, IEEE International Conference on Communication, Control, Computing and Electronics Engineering, pp. 1-8, 2017.
• [28] M. Abdel-Basset, L. A. Shawky, “Flower Pollination algorithm: A comprehensive review”, Artificial Intelligence Review, a part of Springer Nature, 2018.
• [29] H. Bouaziza, M. Berghidab, A. Lemouaric, “Solving the generalized cubic cell formation problem using discrete flower pollination algorithm,” Expert Systems with Applications, Elsevier, Vol. 150 (2020), pp. 1-13.
• [30] M. Abdel-Baset and I. M. Hezam, “An Effective Hybrid Flower Pollination and Genetic Algorithm for Constrained Optimization Problems”, Advanced Engineering Technology and Application, No. 4, pp. 27–34, 2015.
• [31] P. Kielan, D. Mazur, A. Szklarz, “BLDC motor control in HiL configuration with the use of Matlab/Simulink software and PLC”, Przegląd Elektrotechniczny, 06/2017, R. 93, pp. 85-88, 5-8.
• [32] Modeling of DC Motor - National Chiao Tung University,http://ocw.nctu.edu.tw/course/dssi032/DSSI_2.pdf.
• [33] Y-P Chen, “Modeling of DC motor”, Spring Course on Dynamic System Simulation and Implementation, Department of Electrical and Computer Engineering, NCTU, 2015.
• [34] Has Z., Habibidin A. Muslim and Mardiyah N. A., “Adaptive- Fuzzy-PID Controller Based Disturbance Observer for DC Motor Speed Control”, Proceedings of IEECSI, 2017.
• [35] S. Hasan, “Improvement of Transient Response of a DC Motor Controller Based on Non-Linear Optimization Techniques”, International Journal of Engineering and Technical Research, Vol. 8 (2018), No. 11, pp. 08-13.
• [36] G. Sarowar and S. Hasan, “Enhancement of Cruise Control System by Improving Transient Response of Motor Controller using Non-Linear Optimization Techniques”, International Journal of Computer Science and Network Security (IJCSNS), Vol. 18 (2018), No. 12, pp. 145-152.
• [37] D. Yousri, A. Mabrouk, Lobna A. Said, A. G. Radwan, “Biological Inspired Optimization Algorithms for Cole- Impedance Parameters Identification”, International Journal of Electronics and Communications, Vol. 78 (2017), pp. 1-33.
• [38] W. M. Elsrogy, M. A. Fkirin, M. A. M. Hassan. “Speed control of DC motor using PID controller based on artificial intelligence techniques”, IEEE International Conference on Control, Decision and Information Technologies (CoDIT), pp. 196-201, 2013.
• [39] D. C. Meena and A. Devanshu. “Genetic algorithm tuned PID controller for process control”, IEEE International Conference on Inventive Systems and Control (ICISC), pp. 1-6, 2017.
• [40] M. Abdel Basset, I. El Henawy and N. Diab. “Optimal orbital elements of binary stars based on flower pollination algorithm”, International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 9 (2019), No. 1, pp. 56-69.
• [41] C. Jin, K. Gu, Silviu-I. Niculescu, I. Boussaada, “Stability Analysis of Systems with Delay-Dependent Coefficients: An Overview,” IEEE Access, Vol. 6 (2018), pp. 27392 – 27407.
• [42] Godem Ali M. Ismeal, K. Kyslan and V. Fedák, “CAD of Cascade Controllers for DC Drives Using Genetic Algorithm Methods”, Procedia Engineering, Vol. 96 (2014), No. 96, pp. 182-189.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).