EVALUATION OF CONTROLLER PARAMETERS ON THE TWIN ROTOR MULTIPLE INPUT MULTIPLE OUTPUT SYSTEM USING BUTTERFLY-BASED PARTICLE SWARM OPTIMIZATION

Year 2023, , 174 - 189, 29.03.2023

Ali Can Çabuker , Mehmet Nuri Almalı , İshak Parlar

https://doi.org/10.59313/jsr-a.1198441

Abstract

Studies on the control of nonlinear systems with metaheuristic algorithms are increasing day by day. It is one of the nonlinear systems in the Twin rotor multiple input multiple output (TRMS) system, which emerged as a prototype of helicopters. This system has two control angles horizontally and vertically. In this study, the yaw and pitch angle control parameters of the TRMS system were found using both traditional and butterfly-based particle swarm optimization (BFPSO) method. In experimental studies, reference values of main propeller and tail propeller angles were tried to be reached in TRMS with fractional order proportional-integral-derivative (FOPID), proportional-integral-derivative (PID) and tilt-integral-derivative (TID) controllers.

Keywords

TRMS, Pitch Angle, Yaw Angle, BFPSO, PID, FOPID, TID

Thanks

The authors thank the TRMS Application Unit at Van Yuzuncu Yıl University Electronics Laboratory for some of the data presented in this article.

References

• [1] Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks, 4, 1942-1948.
• [2] Bohre, A. K. Agnihotri, G. and Dubey, M. (2014). Hybrid butterfly based particle swarm optimization for optimization problems. In 2014 First International Conference on Networks & Soft Computing, 172-177.
• [3] Bohre, A. K. Agnihotri, G. and Dubey, M. (2015). The butterfly-particle swarm optimization (Butterfly-PSO/BF-PSO) technique and its variables. International Journal of Soft Computing, Mathematics and Control, IJSCMC, 4, 3.
• [4] Mathi, D. K. and Chinthamalla, R. (2020). A hybrid global maximum power point tracking method based on butterfly particle swarm optimization and perturb and observe algorithms for a photovoltaic system under partially shaded conditions. International Transactions on Electrical Energy Systems, 30, 10.
• [5] Agrawal, A. K. (2013). Optimal Controller Design for Twin Rotor MIMO System, Doctoral dissertation.
• [6] Mustapha, S. Fayçal, K. M. and Mohammed, S. (2015). Application of artificial immune algorithm-based optimisation in tuning a PID controller for nonlinear systems. International Journal of Automation and Control, 9, 3, 186-200.
• [7] Ting, T. O. Yang, X. S. Cheng, S. Huang, K. (2015). Hybrid metaheuristic algorithms: past, present, and future. Recent advances in swarm intelligence and evolutionary computation, 71-83.
• [8] Khanduja, N. Bhushan, B. (2021). Optimal design of FOPID Controller for the control of CSTR by using a novel hybrid metaheuristic algorithm. Sādhanā, 46, 2, 1-12.
• [9] TRMS, T. R. M. (2010). System Control Experiments Manuel. 33-949S: Feedback Instruments Ltd. Sussex, UK.
• [10] Tiwalkar, R. G. Vanamane, S. S. Karvekar, S. S. and Velhal, S. B. (2017). Model predictive controller for position control of twin rotor MIMO system. In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI, 952-957.
• [11] Chalupa, P. Přikryl, J. and Novák, J. (2015). Modelling of twin rotor MIMO system. Procedia Engineering, 100, 249-258.
• [12] Wijekoon, J. Liyanage, Y. Welikala, S. and Samaranayake, L. (2017). Yaw and pitch control of a twin rotor MIMO system. In 2017 IEEE International Conference on Industrial and Information Systems, ICIIS, 1-6.
• [13] Chaudhary, S. and Kumar, A. (2019). Control of Twin Rotor MIMO system using 1-degree-of-freedom PID, 2-degree-of-freedom PID and fractional order PID controller. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology, ICECA, 746-751.
• [14] Katoch, S. Chauhan, S. S. and Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80, 5, 8091-8126.
• [15] Wang, D. Tan, D. and Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft computing, 22, 2, 387-408.
• [16] El-Shorbagy, M. A. Hassanien, A. E. (2018). Particle swarm optimization from theory to applications. International Journal of Rough Sets and Data Analysis, IJRSDA, 5, 2, 1-24.
• [17] Jaen-Cuellar, A. Y. de J. Romero-Troncoso, R. Morales-Velazquez, L. and Osornio-Rios, R. A. (2013). PID-controller tuning optimization with genetic algorithms in servo systems. International Journal of Advanced Robotic Systems, 10, 9, 324.
• [18] Meena, D. C. and Devanshu, A. (2017). Genetic algorithm tuned PID controller for process control. In 2017 International Conference on Inventive Systems and Control, ICISC, 1-6.
• [19] Khuwaja, K. Tarca, I. C. and Tarca, R. C. (2018). PID controller tuning optimization with genetic algorithms for a quadcopter. Recent Innovations in Mechatronics, 5, 1, 1-7.
• [20] Abukan, Y. Almalı, M. N. Çabuker, A. C. and Parlar, İ. (2022). Determining The PID Parameters of The TRMS System Using PSO. 1ST INTERNATIONAL CONFERENCE ON ENGINEERING AND APPLIED NATURAL SCIENCES, Konya, Türkiye, 10 - 13 Mayıs 2022, 97-102.
• [21] Abdulhussein, K. G. Yasin, N. M. Hasan, I. J. (2021). Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor. International Journal of Power Electronics and Drive Systems, 12, 2, 736.
• [22] El Hajjami, L. Mellouli, E. M. Berrada, M. (2019). Optimal PID control of an autonomous vehicle using Butterfly Optimization Algorithm BOA. In Proceedings of the 4th international conference on big data and internet of things, 1-5.
• [23] Esgandanian, A. and Daneshvar, S. (2016). A comparative study on a tilt-integral-derivative controller with proportional-integral-derivative controller for a pacemaker. International Journal of Advanced Biotechnology and Research, IJBR, 7, 3, 645-650.
• [24] Aidoud, M. Feliu-Batlle, V. Sebbagh, A. Sedraoui, M. (2022). Small signal model designing and robust decentralized tilt integral derivative TID controller synthesizing for twin rotor MIMO system. International Journal of Dynamics and Control, 1-17.
• [25] Lurie, B. J. (1994). Three-parameter tunable tilt-integral-derivative (TID) controller.
• [26] Yusoff, W. A. W. Yahya, N. M. and Senawi, A. (2006). Tuning of Optimum PID Controller Parameter Using Particle Swarm Optimization Algorithm Approach. Fakulti Kejuruteraan Mekanikal University Malaysia Pahang.
• [27] Faisal, R. F. and Abdulwahhab, O. W. (2021). Design of an adaptive linear quadratic regulator for a twin rotor aerodynamic system. Journal of Control, Automation and Electrical Systems, 32, 2 404-415.
• [28] Bahramipour-Esfahani, R. Nasri, M. Tabatabaei, S. M. (2021). Designing a Metaheuristic Multi-objective Fractional-order PID Controller for TRMS system. Computational Intelligence in Electrical Engineering, 12, 2, 91-112.