- IUT Repository Home
- →
- Electrical and Electronics Engineering (EEE)
- →
- Thesis
- →
- Graduate
- →
- 2019
- →
- View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Hasan, Shameem | |
| dc.date.accessioned | 2020-10-26T07:57:56Z | |
| dc.date.available | 2020-10-26T07:57:56Z | |
| dc.date.issued | 2019-11-15 | |
| dc.identifier.citation | [1] Vedam, N., Diaz-Rodriguez, I., Bhattacharyya S.P., “A novel approach to the design of controllers in an automotive cruise-control system”, Industrial Electronics Society, IECON - 40th Annual Conference of the IEEE, pp. 2927- 2932, 2014. [2] Ramin S. and Wan Ishak W. I. “Design and Simulation of Control Systems for a Field Survey Mobile Robot Platform”, Research Journal of Applied Sciences, Engineering and Technology, 6(13), pp. 2307-2315, 2013. [3] Hasan S., “Improvement of Transient Response of a DC Motor Controller Based on Non-Linear Optimization Techniques”, International Journal of Engineering and Technical Research, 8(11), pp. 08-13, 2018. [4] Sarowar G. and Hasan S., “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), 18(12), pp. 145-152, 2018. [5] Prof. Yon-Ping Chen, “Modeling of DC motor”, Spring Course on Dynamic System Simulation and Implementation, Department of Electrical and Computer Engineering, NCTU, 2015. [6] Dorf, R.C. and R.H. Bishop, “Modern Control Systems”, 9th edition, Prentice-Hall, Inc., Upper Saddle River, NJ, 2001. [7] Nabil E., “A Modified Flower Pollination Algorithm for Global Optimization”, Expert systems with applications, Elsevier, 57, pp. 192-203, 2016. [8] Aiping Shi, Maoli Yan, Jiangyong Li, “The research of fuzzy PID control application in DC motor of automatic doors”, International Conference on Electrical and Control Engineering (ICECE), pp. 1354-1358, 2011. [9] Chengaiah Ch. and Venkateswarlu K., “Comparative study on dc motor speed control using various controllers”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(1), pp. 6918 - 6925, 2014. [10] Abdulameer A., Sulaiman, M., Aras, M., and Saleem, D., “Tuning Methods of PID Controller for DC Motor Speed Control”, Indonesian Journal of Electrical Engineering and Computer Science, 3(2), pp. 343-349, 2016. [11] Venugopal P., “Design of tuning methods of PID controller using fuzzy logic”, International Journal of Emerging trends in Engineering and Development, 3(5), pp. 239-248, 2013. [12] Zhang J., Wang N. and Wang S., “A developed method of tuning PID controllers with fuzzy rules for integrating process”, Proceeding of the American Control Conference, pp. 1109-1114, 2004. [13] Rayapakse A., Furuta K., And Kondo S., “Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution”, IEEE Transaction on Fuzzy Systems, 10(3), pp. 309-321, 2002. [14] Kumar S. B., Ali M. H. and Sinha A., “Design and Simulation of Speed Control of DC Motor by Fuzzy Logic Technique with Matlab/Simulink”, International Journal of Scientific and Research Publications, 4(7), pp. 255-299, 2014. [15] Ang K.H., Chong G. and Li Y., “PID control system analysis: Design and technology”, IEEE Transaction on Control System, 13(4), pp. 559-576, 2004. [16] Thomas N., Poongodi P., “Position control of DC motor using genetic algorithm based PID controller”, Proceedings of the World Congress on Engineering, pp. 1–5, 2009. [17] Reynoso-Meza G., Blasco X., Sanchis J. et al., “Controller tuning using evolutionary multi-objective optimization: current trends and applications”, Control Engineering Practice, 28(1), pp. 58–63, 2014. [18] Padula F., Visioli A., “Optimal tuning rules for proportional-integer derivative and fractional-order proportional-integer-derivative controllers for integral and unstable processes”, IET Control Theory Applications, 6(6), pp. 776 - 786, 2012. [19] El-Deen A. T., Mahmoud A. A. H., El-Sawi A. R., “Optimal PID Tuning for DC Motor Speed Controller Based on Genetic Algorithm”, International Review of Automatic Control (IREACO), 8(1), pp. 745-753, 2015. 68 [20] Zhao D, and Zhang N., “An Improved Nonlinear Speed Controller for Series DC Motors”. Proceedings of the 17th World Congress the International Federation of Automatic Control, Seoul, Korea, 2008. [21] Kara T. and Eker I., “Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments,” Energy Conversion and Management, 45(7-8), pp. 1087-1106, 2004. [22] Siller-Alcala I.I., Liceaga-Castro J.U., Ferreyra-Ramírez A., Alcántara Ramírez R. and Jaimes-Ponce J., “Speed Nonlinear Predictive Control of a Series DC Motor for bidirectional operation”, Recent Researches in Mathematical Methods in Electrical Engineering and Computer Science, pp. 182-187, 2011. [23] Landau I. D. and Zito G., Digital Control Systems, Springer, 2006. [24] Kaya I., Tan N., and Atherton D. P., “Simple procedure for improving performance of PID controllers”, Proceedings of IEEE Conference on Control Application, 2, pp. 882–885, 2003. [25] Fekety F. R., “The clonal selection theory of acquired immunity”, Yale Journal of Biology and Medicine, 32, 1960. [26] De Castro, L. N., & Timmis, J. “Artificial immune systems: A new computational intelligence approach” (2002 ed.). London: New York: Springer, 2002. [27] Gao S., Chai, H., Chen, B., & Yang, G., “Hybrid gravitational search and clonal selection algorithm for global optimization”, Advances in swarm intelligence, Springer, pp. 1-10, 2013. [28] Layeb, A., “A clonal selection algorithm based tabu search for satisfiability problems”, Journal of Advances in Information Technology, 3, pp. 138–146, 2012. [29] Ludwig, S. A., “Clonal selection based genetic algorithm for workflow service selection”, IEEE congress on evolutionary computation (CEC), pp. 1-7, 2013. [30] Yuhui, S., “An optimization algorithm based on brainstorming process”, International Journal of Swarm Intelligence Research, 2, 35–62, 2011. [31] Qin, S., Zhou, L.-S., & Yue, Y.-X., “A clonal selection based differential evolution algorithm for doubletrack railway train schedule optimization” In Proceedings of the 2nd international conference on advanced computer control (ICACC), pp. 155–158, 2010. [32] Yang, X.-S., “A new metaheuristic bat-inspired algorithm”, Nature inspired cooperative strategies for optimization (NICSO), Springer, pp. 65 – 74, 2010. [33] Franklin G. F., “Feedback Control of Dynamic Systems”, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2nd edition, 1993. [34] Nise N. S., “Control System Engineering”, John Wiley and Sons Inc., New York, 2011. [35] Zanasi R., Cuoghi S., “Analytical and Graphical Design of PID Compensators on the Nyquist plane”, IFAC Conference on Advances in PID Control, 2012. [36] Zhang W., Xi Y., Yang G., Xu X., “Design PID controllers for desired time-domain or frequency-domain response”, The Instrumentation, Systems, and Automation Society Transactions, 41, pp. 511–520, 2002. [37] Ogata K., “Modern Control Engineering, 4th Edition”, 2015, Dorling Kindersley Pvt. Ltd., India. [38] Shahzad U., “Design and Analysis of a Control System Using Root Locus and Frequency Response Methods”, Journal of Information Technology & Electrical Engineering, 6(4), pp. 14-19, 2017. [39] Nakhmani A. Zeheb E., “Generalized Nyquist Criterion and Generalized Bode Diagram for Analysis and Synthesis of Uncertain Control Systems”, 24th IEEE convention on Electrical and Electronics Engineers, pp. 250-254, 2006. [40] Yanqiu C., Yaning Y., Zhang T., “Computer-aided design of lead compensator of control system via MATLAB”, International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), pp. 2576-2578, 2011. [42] Alfaro V. M., “PID controllers’ fragility”. ISA transactions, 46(4), pp. 555–559, 2007. [43] Franklin G. F., Powell J. D., and A. Emami-Naeini A., “Feedback Control of Dynamic Systems”, 6th Edition. Pearson, Upper Saddle River, NJ, 2010. [44] Padula, F. and Visioli A., “Tuning rules for optimal PID and fractional-order PID controllers”. Journal of Process Control, 21(1), pp. 69 –81, 2011. 69 [45] Ziegler, J. G. and Nichols N. B., “Optimum Settings for Automatic Controllers”, Transactions on ASME, 64(11), pp. 759–768, 1942. [46] V. M. and Vilanova R., “Performance and Robustness Considerations for Tuning of Proportional Integral/Proportional Integral Derivative Controllers with Two Input Filters”. Industrial & Engineering Chemistry Research, 52(51), pp. 18287–18302, 2013. [47] Astrom K. J., Panagopoulos H., and Hagglund T., “Design of PID Controllers based on Non-Convex Optimization”, Automatica, 34(5), pp. 585–601, 2005. [48] Garpinger O., “Design of Robust PID Controllers with Constrained Control Signal Activity”, Lund University, 2010. [49] Garpinger O., Hagglund T., and Astrom K. J., “Performance and robustness trade-offs in PID control”. Journal of Process Control, 24(5), pp. 568–577, 2014. [50] Hagglund T. and Astrom K. J., “Revisiting the Ziegler-Nichols step response method for PID control”. Journal of Process Control, 14(6), pp. 635–650, 2004. [51] Pongfai J. and Assawinchaichote W., “Self-tuning PID parameters using NN-GA for Brush DC motor control system”, International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2017. [52] Grimholt C. and Skogestad S., “Improved Optimization-based Design of PID Controllers Using Exact Gradients”, 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering, 2015. [53] Abdullah Y. Al-Maliki and Kamran Iqbal, “PID-Type FLC Controller Design and Tuning for Sensorless Speed Control of DC Motor”, Advances in Science, Technology and Engineering Systems Journal, 3(6), pp. 515-522, 2018. [54] Sumardi, Wahyudi, Rosaliana M. et al. “Self-Tuning Fuzzy PID Design for BLDC Speed Control”, Global Research and Development Journal for Engineering, 3(4), pp. 4-11, 2018. [55] Khan A. A. and Rafay Khan U. G. M., “Speed Control of DC Motor Under Varying Load Using PID Controller,” International Journal of Engineering, 9(3), 2015. [56] Segovia R., Hagglund T., and Astrom K. J., “Noise filtering in PI and PID Control”. American Control Conference, pp. 1763-1770, 2013. [57] Sanchis R., Romero J. A., and Balaguer P., “Tuning of PID controllers based on simplified single parameter optimization”. International Journal of Control, 83(9), pp. 1785–1798, 2010. [58] Skogestad S., “Simple analytic rules for model reduction and PID controller tuning”. Journal of Process Control, 13(4), pp. 291–309, 2003. [59] Skogestad S. and Grimholt C., “The SIMC Method for Smooth PID Controller Tuning”, PID Control in the Third Millennium, Springer-Verlag, pp. 147–175, 2012. [60] Sekara T. B. and Matausek M. R., “Optimization of PID controller based on maximization of the proportional gain under constraints on robustness and sensitivity to measurement noise”. IEEE Transactions on Automatic Control, 54(1), pp. 184-189, 2009. [61] Astrom K., Hagglund T., “PID Controllers; Theory, Design and Tuning”, Instrument Society of America, 1995. [62] O’Mahony T., Downing C. J. and Fatla K., “Genetic Algorithm for PID Parameter Optimization: Minimizing Error Criteria”, Process Control and Instrumentation, pp. 148-153, 2000. [63] Gotshall S. and Rylander B., “Optimal Population Size and The Genetic Algorithm.”, Proceedings On Genetic and Evolutionary Computation Conference, 2000. [64] Herrero J.M., Blasco X, Martinez M and Salcedo J.V., “Optimal PID Tuning with Genetic Algorithm for Non Linear Process Models.” 15th Triennial World Congress, 2002. [65] Naeem W., Sutton R. Chudley J., Dalgleish F.R. and Tetlow S., “An Online Genetic Algorithm Based Model Predictive Control Autopilot Design with Experimental Verification”, International Journal of Control, 78(14), pp. 1076 – 1090, 2005. 70 [66] Skogestad S., “Probably the Best Simple PID Tuning Rules in The World”, Journal of Process Control, 2001. [67] Lin C., Jan H., and Shieh N., “GA-based multi objective PID control for a linear brushless dc motor,” IEEE/ASME Transactions on Mechatronics, 8, pp. 56-65, 2003. [68] Hameed H. S., “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. [69] Pongfai J. and Assawinchaichote W. “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, 2018. [70] Chaudhary H., Khatoon S and Singh R. “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. [71] Rout M. K., Sain D., Swain S. K. and Mishra S. K., “PID Controller Design for Cruise Control System using Genetic Algorithm”, IEEE International Conference on Electrical, Electronics, and Optimization Techniques, pp. 4170 - 4175, 2016. [72] 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. [73] Adhikari N. P., Choubey M. and Singh R., “DC Motor Control Using Ziegler Nichols and Genetic Algorithm Technique”, International Journal of Electrical, Electronics and Computer Engineering, 1(1), pp. 33-36, 2012. [74] Meshram P. M. and Kanojiya R. G., “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. [75] “Speed Control of DC Motor Using Fuzzy Logic Controller”, IEEE International Conference on Communication, Control, Computing and Electronics Engineering, 2017. [76] Kennedy J., “Particle swarm optimization”, Encyclopedia of Machine Learning, Springer, pp. 760–766, 2011. [77] Mirjalili S., Mirjalili S.M., Lewis A., “Grey wolf optimizer”, Advances in Engineering Software, 69, pp. 46- 61, 2014. [78] Kalra, S., Arora, S., “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. [79] Yang X. S., “A new metaheuristic bat-inspired algorithm”, Nature Inspired Cooperative Strategies for Optimization, Springer, pp. 65–74, 2010. [80] Gandomi A.H., Yang X.S., Alavi A.H., “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems”, Engineering with Computers, 29(1), pp. 17–35, 2013. [81] Yang, X.S., “Flower pollination algorithm for global optimization”, International Conference on Unconventional Computing and Natural Computation, Springer, pp. 240–249, 2012. [82] Abdel-Baset M. and Hezam I. M., “An Effective Hybrid Flower Pollination and Genetic Algorithm for Constrained Optimization Problems”, Advanced Engineering Technology and Application, 4, pp. 27–34, 2015. [83] Shah-Hosseini H., “Problem solving by intelligent water drops”, Evolutionary Computation, IEEE Congress On. IEEE, pp. 3226–3231, 2007. [84] Xu, Y., Cui Z., Zeng J., “Social emotional optimization algorithm for nonlinear constrained optimization problems”, Swarm, Evolutionary, and Memetic Computing. Springer, pp. 583–590, 2010. 71 [85] He X. S., Yang X. S., Karamanoglu, M., Zhao, Y.X., “Global convergence analysis of the flower pollination algorithm: a discrete-time Markov chain approach, Proceedings of Computer Science, 108, pp. 1354-1363, 2017. [86] Wang R., Zhou Y., Qiao S., Huang K., “Flower pollination algorithm with bee pollinator for cluster analysis”, Information Processing Letters, 116(1), pp. 1–14, 2016. [87] Ram J. P., Babu, T. S., Dragicevic T., Rajasekar N., “A new hybrid bee pollinator flower pollination algorithm for solar pv parameter estimation”, Energy Conversion Management, 135, pp. 463–476, 2017. [88] Salgotra R., Singh U., “Application of mutation operators to flower pollination algorithm”, Expert System Applications, 79, pp. 112–129, 2017. [89] Xu S., Wang Y., Huang F., “Optimization of multi-pass turning parameters through an improved flower pollination algorithm”, International Journal of Advanced Manufacturing Technology, pp. 1–12, 2016. [90] Abdel-Basset M., Shawky L. A., “Flower Pollination algorithm: A comprehensive review”, Artificial Intelligence Review, a part of Springer Nature, 2018. [91] Pop C. B., Chifu V. R., Salomie I., Racz D. S., Bonta R. M., “Hybridization of the flower pollination algorithm - a case study in the problem of generating healthy nutritional meals for older adults”, Natureinspired computing and optimization. Springer, pp 151-183, 2017. [92] Singh U., Salgotra R., “Synthesis of linear antenna array using flower pollination algorithm”, Neural Computational Applications, 29(2), pp. 435 – 445, 2018. [93] Xu S., Wang Y., Huang F., “Optimization of multi-pass turning parameters through an improved flower pollination algorithm”, International Journal of Advanced Manufacturing Technology, 89 (1–4), pp. 503 – 514, 2017. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/562 | |
| dc.description | Supervised by Prof. Dr. Golam Sarowar | en_US |
| dc.description.abstract | Due to their better reliabilities and low costs, nowadays, DC motors are widely used in industrial applications, robot manipulators, and home appliances. There are several types of applications where the load on the DC motor varies over a speed range. These applications demand speed control accuracy and excellent dynamic responses. So, it is vital to introduce a controller to control the speed and transient behavior of a DC motor in the desired manner. In this study, for the enhancement of dynamic response, various DC motor controllers have been simulated. At first, the DC motor parameters are selected and four optimized controllers: Ziegler-Nichols (ZN) method based PID controller, Genetic Algorithm (GA), Flower Pollination Algorithm (FPA) and Modified Flower Pollination Algorithm (MFPA) are designed and simulated in order to control the angular speed and dynamic response of the shaft of a DC motor actuator. Ziegler-Nichols (ZN) sustained oscillation method provided better tuning parameters than the Ziegler-Nichols 1st method. Here, the Genetic Algorithm (GA) provides 7.558 times better rise time, 16.07 times better settling time and 1.48 times better overshoot than the Ziegler-Nichols (ZN) sustained oscillation method based conventional PID controller. The Flower Pollination Algorithm (FPA) provides 7.593 times better rise time, 13.55 times better settling time and 1.41 times better overshoot than Ziegler-Nichols (ZN) sustained oscillation method. Flower Pollination Algorithm provides 1.0046 times better rise time than Genetic Algorithm (GA) based controller. Modified Flower Pollination Algorithm (MFPA) produces 2.2 times better overshoot than the Genetic Algorithm (GA) with acceptable rise and settling time. Modified Flower Pollination Algorithm (MFPA) produces 2.23 times better overshoot and 1.1 times better settling time than the Flower Pollination Algorithm (FPA) based controller. The response of the actuator for each controller is ascertained and compared applying a step input that simulated transient response of the motor. The performance analysis shows that the Modified Flower Pollination Algorithm is adequate for the wheel steering task, which requires precision and associated with transient response properties. It seems reasonable to conclude that the proposed design criteria are effective and satisfactory. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Department of Electrical and Electronic Engineering, Islamic University of Technology,Board Bazar, Gazipur, Bangladesh | en_US |
| dc.title | Improvement of Transient Response of DC Motor Controller Based on Modified Non-linear Optimization Techniques | en_US |
| dc.type | Thesis | en_US |
Files in this item
This item appears in the following Collection(s)
-
2019 [17]