Recent metaheuristics on control parameter determination
Metaheuristics have been widely used in recent years for tuning control parameters since they have a simple structure, are easy to apply, and provide efficient solutions. In this study, control of a two-wheeled mobile robot using the inverted pendulum principle is proposed. The performances of nine recent metaheuristics (Political Optimizer, Equilibrium Optimizer, Aquila Optimizer, Flow Directional Algorithm, Cheetah Optimizer, Golden Jackal Optimizer, Artificial Rabbit Optimization, Gazelle Optimizer, and Pelican Optimization) have been investigated for the balancing and speed control of a two-wheeled vehicle. In this context, a framework consisting of two cascaded PI controllers is designed to provide balance and speed control of the two-wheeled vehicle. The performances of the recent metaheuristics are also compared with previously introduced effective metaheuristic algorithms for further evaluation. The parameters of the controllers are tuned by using these metaheuristics. In experimental studies, quantitative and qualitative analyses are performed for evaluation of the metaheuristics. The dynamic system properties, convergence curves, computational times, and statistical results are provided to prove optimal control performances. The results show that 11 out of 14 compared algorithms produce similar optimal results in speed and balance control of the two-wheeled vehicle. The rest of them do not provide satisfactory results for the tuning of optimum control parameters of the two-wheeled vehicle.
[1] Nssibi, M., Manita, G., & Korbaa, O. (2023). Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey. Computer Science Review, 49, 100559
[2] Meenachi, L., & Ramakrishnan, S. (2021). Metaheuristic search based feature selection methods for classification of cancer. Pattern Recognition, 119, 108079.
[3] Yuan, X., Hu, G., Zhong, J., & Wei, G. (2023). HBWO-JS: jellyfish search boosted hybrid beluga whale optimization algorithm for engineering applications. Journal of Computational Design and Engineering, 10(4), 1615-1656.
[4] Han, M., Du, Z., Zhu, H., Li, Y., Yuan, Q., & Zhu, H. (2022). Golden-Sine dynamic marine predator algorithm for addressing engineering design optimization. Expert Systems with Applications, 210, 118460.
[5] Deng, L., & Liu, S. (2023). Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design. Expert Systems with Applications, 225, 120069.
[6] Calgan, H. (2023). Optimal C-type filter design for wireless power transfer system by using support vector machines. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 13(2), 151-160.
[7] Perin, D., Karaoglan, A. D., & Yilmaz, K. (2024). Rotor design optimization of a 4000 rpm permanent magnet synchronous generator using moth flame optimization algorithm. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 14(2), 123-133.
[8] Riaz, M., Bashir, M., & Younas, I. (2022). Metaheuristics based COVID-19 detection using medical images: A review. Computers in Biology and Medicine, 144, 105344.
[9] Olmez, Y., Koca, G. O., Tanyildizi, E., & Sengur, A. (2023). Multilevel image thresholding based on Renyi’s entropy and golden sinus algorithm II. Neural Computing and Applications, 35(24), 17837- 17850.
[10] Ozmen Koca, O. K., Dogan, S., & Yilmaz, H. (2018). A multi-objective route planning model based on genetic algorithm for cuboid surfaces. Automatika: časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 59(1), 120-130.
[11] Olmez, Y., Koca, G. O., Sengur, A., & Acharya, U. R. (2023). PS-VTS: particle swarm with visit table strategy for automated emotion recognition with EEG signals. Health Information Science and Systems, 11(1), 22.
[12] Yigit, T., & Celik, H. (2020). Speed controlling of the PEM fuel cell powered BLDC motor with FOPI optimized by MSA. International Journal of Hydrogen Energy, 45(60), 35097-35107.
[13] Arya, Y. (2018). AGC of two-area electric power systems using optimized fuzzy PID with filter plus double integral controller. Journal of the Franklin Institute, 355(11), 4583-4617.
[14] Mohanty, B., Panda, S., & Hota, P. K. (2014). Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. International journal of electrical power & energy systems, 54, 77-85.
[15] Sathya, M. R., & Ansari, M. M. T. (2015). Load frequency control using Bat inspired algorithm based dual mode gain scheduling of PI controllers for interconnected power system. International Journal of Electrical Power & Energy Systems, 64, 365-374.
[16] Sahu, R. K., Panda, S., & Padhan, S. (2015). A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. International Journal of Electrical Power & Energy Systems, 64, 9-23.
[17] Dash, P., Saikia, L. C., & Sinha, N. (2015). Comparison of performances of several FACTS devices using Cuckoo search algorithm optimized 2DOF controllers in multi-area AGC. International Journal of Electrical Power & Energy Systems, 65, 316-324.
[18] Dash, P., Saikia, L. C., & Sinha, N. (2015). Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller. International Journal of Electrical Power & Energy Systems, 68, 364-372.
[19] Puangdownreong, D., Nawikavatan, A., & Thammarat, C. (2016). Optimal design of I-PD controller for DC motor speed control system by cuckoo search. Procedia Computer Science, 86, 83- 86.
[20] Sahoo, B. P., & Panda, S. (2018). Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control. Sustainable Energy, Grids and Networks, 16, 278-299.
[21] Wang, L., Ni, H., Zhou, W., Pardalos, P. M., Fang, J., & Fei, M. (2014). MBPOA-based LQR controller and its application to the double-parallel inverted pendulum system. Engineering Applications of Artificial Intelligence, 36, 262-268.
[22] Demirtaş, M., & Ahmad, F. (2023). Fractional fuzzy PI controller using particle swarm optimization to improve power factor by boost converter. An International Journal of Optimization and Control: Theories & Applications (IJOCTA).
[23] Özyetkin, M. M., & Birdane, H. (2023). The processes with fractional order delay and PI controller design using particle swarm optimization. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 13(1), 81-91.
[24] Askari, Q., Younas, I., & Saeed, M. (2020). Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowledge-based systems, 195, 105709.
[25] Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020). Equilibrium optimizer: A novel optimization algorithm. Knowledge-based systems, 191, 105190.
[26] Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-Qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: a novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250.
[27] Karami, H., Anaraki, M. V., Farzin, S., & Mirjalili, S. (2021). Flow direction algorithm (FDA): a novel optimization approach for solving optimization problems. Computers & Industrial Engineering, 156, 107224.
[28] Akbari, M. A., Zare, M., Azizipanah-Abarghooee, R., Mirjalili, S., & Deriche, M. (2022). The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems. Scientific reports, 12(1), 10953.
[29] Wang, L., Cao, Q., Zhang, Z., Mirjalili, S., & Zhao, W. (2022). Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 114, 105082.
[30] Chopra, N., & Ansari, M. M. (2022). Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 198, 116924.
[31] Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2023). Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer. Neural Computing and Applications, 35(5), 4099-4131.
[32] Trojovský, P., & Dehghani, M. (2022). Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications. Sensors, 22(3), 855.
[33] Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & structures, 169, 1-12.
[34] Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67.
[35] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.
[36] Yang, X. S. (2012, September). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Berlin, Heidelberg: Springer Berlin Heidelberg.
[37] Hansen, N. (2016). The CMA evolution strategy: A tutorial. arXiv preprint arXiv:1604.00772.
[38] Olmez, Y., Koca, G. O., & Akpolat, Z. H. (2022). Clonal selection algorithm based control for two-wheeled self-balancing mobile robot. Simulation Modelling Practice and Theory, 118, 102552.
[39] Sharma, A., & Singh, N. (2024). Load frequency control of connected multi-area multi-source power systems using energy storage and lyrebird optimization algorithm tuned PID controller. Journal of Energy Storage, 100, 113609.
[40] Ozmen Koca, G., & Korkmaz, D. (2019). Neural network based control of a two-mass drive system. International Journal of Intelligent Systems and Applications in Engineering, 7(2), 92-98.