Design of an Optimal Controller for a Propeller-Pendulum System using the PSO Technique

محتوى المقالة الرئيسي

Waleed Al-Ashtari

الملخص

A new model for the non-linear propeller-pendulum system is derived in this study. The model takes into consideration the effects of external disturbances and the properties of the pendulum elements. Two systems with PID controllers are simulated. In the first system, Simulink is used to implement the system and tune the PID parameters. In the second system, a MATLAB script is used to simulate the system and tune the PID parameters. The scrip uses the Runge-Kutta method for solving the system’s equation and uses particle swarm optimization (PSO) to tune the parameters. Further, both systems were investigated under two disturbance conditions. The performance of these systems is evaluated based on comparing the settling time, the peak overshoot, and the integral of the absolute errors (IAE). The results show that when there is no disturbance, both systems are capable of tracking the desired signal successfully. However, the results also show that the application of disturbances causes the first system to lose its smooth response. In contrast, the second system demonstrates a robust response and effective countermeasures to disturbance effects. The results of unit step disturbance are as follows: the settling time, peak overshoot, and IAE of the first system are 13.16s, 11.6%, and 1.706, respectively. Further, the settling time, peak overshoot, and IAE of the second system are 2.388s, 6.6%, and 0.299, respectively. It can be concluded that Simulink is not recommended to be used for tuning the PID controller in the presence of disturbances.

تفاصيل المقالة

القسم

Articles

كيفية الاقتباس

"Design of an Optimal Controller for a Propeller-Pendulum System using the PSO Technique" (2024) مجلة الهندسة, 30(10), ص 169–183. doi:10.31026/j.eng.2024.10.10.

المراجع

Abdulwahhab O. W. and Abbas N. H., 2020. Survey study of factional order controllers. Journal of Engineering, 26(4), pp. 188–201. https://doi.org/10.31026/j.eng.2020.04.13.

Ahmad, A., Rafiuddin, N. and Khan, Y.U., 2021, December. Comparative analysis of ANN and PID controller of aero-pendulum on simscape. In 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), pp. 334-338. https://doi.org/10.1109/ICCCA52192.2021.9666408.

Ahmed, A.K. and Al-Khazraji, H., 2023. Optimal control design for propeller pendulum systems using gorilla troops optimization. Journal Européen des Systèmes Automatisés, 56(4). https://doi.org/10.18280/jesa.560407.

Al-Araji A. S. and Ibraheem B. A., 2019. A comparative study of various intelligent optimization algorithms based on path planning and neural controller for mobile robot. Journal of Engineering, 25(8), pp. 80–99. https://doi.org/10.31026/j.eng.2019.08.06.

Al-Khazraji, H., Khlilb, S., Alabacy, Z., 2022. Solving mixed-model assembly lines using a hybrid of ant colony optimization and greedy algorithm. Engineering and Technology Journal, 40(1), pp. 172-180. http://doi.org/10.30684/etj.v40i1.2153.

Al-Qassar, A.A., Al-Dujaili, A.Q., Hasan, A.F., Humaidi, A.J., Ibraheem, I.K. and Azar, A.T., 2021. Stabilization of single-axis propeller-powered system for aircraft applications based on optimal adaptive control design. Journal of Engineering Science and Technology, 16(3), pp.1851-1869.

Bharathi, P., Ramachandran, M., Ramu, K. and Chinnasamy, S., 2022. A study on various particle swarm optimization techniques used in current scenario. Des. Model. Fabr. Adv. Robot, 1, pp.15-26.

Borase, R.P., Maghade, D.K., Sondkar, S.Y. and Pawar, S.N., 2021. A review of PID control, tuning methods and applications. International Journal of Dynamics and Control, 9, pp. 818-827. http://dx.doi.org/10.1007/s40435-020-00665-4.

Dakheel, H.S., Abdullah, Z.B., Jasim, N.S. and Shneen, S.W., 2022. Simulation model of ANN and PID controller for direct current servo motor by using Matlab/Simulink. TELKOMNIKA (Telecommunication Computing Electronics and Control), 20(4), pp. 922-932. http://dx.doi.org/10.12928/telkomnika.v20i4.23248.

Fernandez Cornejo E. R., Diaz R. C. and Alama W. I., 2020. PID tuning based on classical and meta-heuristic algorithms: A performance comparison. 2020 IEEE Engineering International Research Conference (EIRCON), Lima, Peru, 2020, pp. 1-4, https://doi.org/10.1109/EIRCON51178.2020.9253750.

Ghith, E.S. and Tolba, F.A.A., 2023. Tuning PID controllers based on hybrid arithmetic optimization algorithm and artificial gorilla troop optimization for micro-robotics systems. IEEE Access, 11, pp. 27138-27154. https://doi.org/10.1109/ACCESS.2023.3258187.

Günel, O., Ankarah, A., 2017. Tuning PID controller using genetic algorithm and particle swarm optimization algorithm for single propeller pendulum. In 3rd Conference on Advances in Mechanical Engineering Istanbul 2017, 19-21 December 2017, Istanbul, Turkey.

Hamoudi A. K., 2016. Design and simulation of sliding mode fuzzy controller for nonlinear system. Journal of Engineering, 22(3), pp. 66–76. https://doi.org/10.31026/j.eng.2016.03.05.

Hamoudi, A.K. and Rasheed, L.T., 2023. Design and implementation of adaptive backstepping control for position control of propeller-driven pendulum system. Journal Européen des Systèmes Automatisés, 56(2), p. 281. https://doi.org/10.18280/jesa.560213.

Lucena, E.R., Luiz, S.O. and Lima, A.M., 2021. Modeling, parameter estimation, and control of an aero-pendulum. In Simpósio Brasileiro de Automação Inteligente-SBAI, l. 1(1). https://doi.org/10.20906/sbai.v1i1.2837.

Mishra, D.P., Raut, U., Gaur, A.P., Swain, S. and Chauhan, S., 2023. Particle swarm optimization and genetic algorithms for PID controller tuning. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 189-194. https://doi.org/10.1109/ICSSIT55814.2023.10060892.

Mohammadbagheri, A., Yaghoobi, M., 2011. A new approach to control a driven pendulum with the PID method. In 13th International Conference on Modelling and Simulation, Cambridge, UK, pp. 207-211. https://doi.org/10.1109/UKSIM.2011.47.

Mostafa, R.R., Gaheen, M.A., Abd ElAziz, M., Al-Betar, M.A. and Ewees, A.A., 2023. An improved gorilla troops optimizer for global optimization problems and feature selection. Knowledge-Based Systems, 269, p.110462. https://doi.org/10.1016/j.knosys.2023.110462.

Mustafa, N. and Hashim, F.H., 2020. Design of a predictive PID controller using particle swarm optimization. International Journal of Electronics and Telecommunications, 66(4), pp.737-743. https://doi.org/10.24425/ijet.2020.134035.

Nagaraj, B. and Murugananth, N., 2010. A comparative study of PID controller tuning using GA, EP, PSO, and ACO. In 2010 International Conference on Communication Control and Computing Technologies, Nagercoil, India, pp. 305-313. https://doi.org/10.1109/ICCCCT.2010.5670571.

Oliveira, E.J., Honorio, L.M., Anzai, A.H. and Soares, T.X., 2014. Linear programming for optimum PID controller tuning. Applied Mathematics, 5(6). https://doi.org/10.4236/am.2014.56084.

Özdemir, C., Öztürk, S., Şengül, Ö. And Kuncan, F., 2022. Position control of the suspended pendulum system with particle swarm optimization algorithm. El-Cezeri Journal of Science and Engineering, 9(2), pp.669-679. https://doi.org/10.31202/ecjse.993313.

Pawan, Y.N., Prakash, K.B., Chowdhury, S. and Hu, Y.C., 2022. Particle swarm optimization performance improvement using deep learning techniques. Multimedia Tools and Applications, 81(19), pp. 27949-27968. https://doi.org/10.1007/s11042-022-12966-1.

Phu, N.D., Hung, N.N., Ahmadian, A. and Senu, N., 2020. A new fuzzy PID control system based on fuzzy PID controller and fuzzy control process. International Journal of Fuzzy Systems, 22(7), pp.2163-2187. https://doi.org/10.1007/s40815-020-00904-y.

Rafiuddin, N. and Khan, Y.U., 2023. Nonlinear controller design for mechatronic aeropendulum. International Journal of Dynamics and Control, 11(4), pp.1662-1670. https://doi.org/10.1007/s40435-022-01080-7.

Rahayu, E.S., Ma’arif, A. and Cakan, A., 2022. Particle swarm optimization (PSO) tuning of PID control on DC motor. International Journal of Robotics and Control Systems, 2(2), pp. 435-447. https://doi.org/10.31763/ijrcs.v2i2.476.

Reynoso-Meza, G., Carrillo-Ahumada, J., Alves Ribeiro, V.H. and Marques, T., 2022. Multi-objective PID Controller Tuning for Multi-model Control of Nonlinear Systems. SN Computer Science, 3(5), p.351. https://doi.org/10.1007/s42979-022-01236-4.

Saleem, O., Rizwan, M., Zeb, A.A., Ali, A.H. and Saleem, M.A., 2020. Online adaptive PID tracking control of an aero-pendulum using PSO-scaled fuzzy gain adjustment mechanism. Soft Computing, 24, pp.10629-10643. https://doi.org/10.1007/s00500-019-04568-1.

Salem, O., Hasan, A., Edden, N.Z., Zedan, S., Dradi, M., Alsadi, S., Foqha, T. and Amer, A., 2024. Propeller Pendulum Control by Matlab Simulink. In: Khoury, R.E., Nasrallah, N. (eds) Intelligent Systems, Business, and Innovation Research. Studies in Systems, Decision and Control, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-36895-0_46.

Salman Z. S. and Saleh M. H., 2022. Attitude and altitude control of quadrotor carrying a suspended payload using genetic algorithm. Journal of Engineering, 28(5), pp. 25–40. https://doi.org/10.31026/j.eng.2022.05.03.

Saud L. J. and Hasan A. F., 2018. Design of an optimal integral backstepping controller for a quadcopter. Journal of Engineering, 24(5), pp. 46–65. https://doi.org/10.31026/j.eng.2018.05.04.

Saud L. J. and Mohammed R. S., 2017. Performance evaluation of a PID and a fuzzy PID controllers designed for controlling a simulated quadcopter rotational dynamics model. Journal of Engineering, 23(7), pp. 74–93. https://doi.org/10.31026/j.eng.2017.07.05.

Shami, T.M., El-Saleh, A.A., Alswaitti, M., Al-Tashi, Q., Summakieh, M.A. and Mirjalili, S., 2022. Particle swarm optimization: A comprehensive survey. IEEE Access, 10, pp.10031-10061. https://doi.org/10.1109/ACCESS.2022.3142859.

Taskin, Y., 2017. Fuzzy PID controller for propeller pendulum. Istanbul University - Journal of Electrical and Electronics Engineering (IU-JEEE), 17(1): 3175-3180.

Wang, D., Tan, D. and Liu, L., 2018. Particle swarm optimization algorithm: an overview. Soft computing, 22, pp.387-408. https://doi.org/10.1007/s00500-016-2474-6.

Wang, L., Luo, Y. and Yan, H., 2023. Ant colony optimization-based adjusted PID parameters: a proposed method. PeerJ Computer Science, 9, p.e1660. https://doi.org/10.7717/peerj-cs.1660.

Zhang, X.L. and Zhang, Q., 2021. Optimization of PID parameters based on ant colony algorithm. In 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 850-853. https://doi.org/10.1109/ICITBS53129.2021.00211.

المؤلفات المشابهة

يمكنك أيضاً إبدأ بحثاً متقدماً عن المشابهات لهذا المؤلَّف.