Mejia-Delgadillo, Marcos | CINVESTAV |
Garrido, Rubén | CINVESTAV |
Mezura-Montes, Efrén | Universidad Veracruzana |
https://doi.org/10.58571/CNCA.AMCA.2024.043
Resumen: This work focuses on tuning of an I-PD controller using the Differential Evolution (ED) algorithm. The cost function employed in this research aims to minimize both the tracking error and the energy consumption of the controller. The ED algorithm shows satisfactory results in finding suitable gain values for the I-PD controller, which were validated on an experimental platform. Additionally, the study highlights the importance of incorporating the stability conditions imposed to the controller gains as constraints in the tuning process to ensure that the solutions provided by the ED algorithm produce a stable closed-loop system.
¿Cómo citar?
Mejia Delgadillo, M., Garrido Moctezuma, R.A. & Mezura Montes, E. (2024). Tuning of an I-PD Controller Applied to a Servo System Using the Differential Evolution Algorithm. Memorias del Congreso Nacional de Control Automático 2024, pp. 244-249. https://doi.org/10.58571/CNCA.AMCA.2024.043
Palabras clave
I-PD control, Differential Evolution, Servo System, Controller Tuning, Real-Time Control
Referencias
- Agarwal, J., Parmar, G., Gupta, R., and Sikander, A. (2018). Analysis of grey wolf optimizer based fractional order pid controller in speed control of dc motor. Microsystem Technologies, 24, 4997–5006.
- Belegundu, A.D. and Chandrupatla, T.R. (2019). Optimization concepts and applications in engineering. Cambridge University Press.
- Celik, E. and Karayel, M. (2024). Effective speed control of brushless dc motor using cascade 1pdf-pi controller tuned by snake optimizer. Neural Computing and Applications, 1–16.
- Das, S. and Suganthan, P.N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4–31. doi: 10.1109/TEVC.2010.2059031.
- He, Q., Li, X., Mao, W., Yang, X., and Wu, H. (2024). Research on vehicle frame optimization methods based on the combination of size optimization and topology optimization. World Electric Vehicle Journal, 15(3), 107.
- Hekimoglu, B. (2019). Optimal tuning of fractional order pid controller for dc motor speed control via chaotic atom search optimization algorithm. IEEE access, 7, 38100–38114.
- Hubálovská, M., Hubálovsky, S., and Trojovsky, P. (2024). Botox optimization algorithm: A new humanbased metaheuristic algorithm for solving optimization problems. Biomimetics, 9(3), 137.
- Insley, B., Bartkoski, D., Balter, P., Prajapati, S., Tailor, R., Jaffray, D., and Salehpour, M. (2024). Numerical optimization of longitudinal collimator geometry for novel x-ray field. Physics in Medicine & Biology, 69(10), 105004.
- Kitade, T., Kanda, R., Matsui, K., Nishikawa, A., Loesing, A., Fukunaga, I., and Sekiguchi, H. (2024). Opti mization of gate structure for damageless microled thin films in optogenetic applications. physica status solidi (a), 2300834.
- Li, J., Soradi-Zeid, S., Yousefpour, A., and Pan, D. (2024). Improved differential evolution algorithm based convolutional neural network for emotional analysis of music data. Applied Soft Computing, 153, 111262.
- Lu, Z., Xu, Z., Zhao, D., and Yang, T. (2024). Ankle joint motion recognition system and algorithm optimization based on plantar pressure. Journal of Mechanics in Medicine and Biology.
- Morari, M. (1983). Design of resilient processing plants—iii: A general framework for the assessment of dynamic resilience. Chemical Engineering Science, 38(11), 1881–1891.
- Ogata, K. (2002). Modern control engineering. Prentice Hall.
- Price, K., Storn, R.M., and Lampinen, J.A. (2006). Differential Evolution: A Practical Approach to Global Optimization. Springer Science & Business Media.
- Puangdownreong, D. (2018). Optimal pid controller design for dc motor speed control system with tracking and regulating constrained optimization via cuckoo search. Journal of Electrical Engineering and Technology, 13(1), 460–467.
- Razmjooy, N., Vahedi, Z., Estrela, V.V., Padilha, R., and Monteiro, A.C.B. (2020). Speed control of a dc motor using pid controller based on improved whale optimization algorithm. In Metaheuristics and Optimization in Computer and Electrical Engineering, 153– 167. Springer.
- Rodríguez-Molina, A., Villarreal-Cervantes, M.G., and Aldape-Pérez, M. (2019). An adaptive control study for the dc motor using meta-heuristic algorithms. Soft Computing, 23, 889–906.
- Storn, R. and Price, K. (1997). Differential evolution- a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
- Su, C.J. and Zhao, T. (2024). Collaborative optimization of thermal conductivity distribution and heat source layout based on bayesian optimization. International Journal of Heat and Mass Transfer, 224, 125324.
- Youla, D., Bongiorno, J., and Jabr, H. (1976). Modern wiener–hopf design of optimal controllers part i: The single-input-output case. IEEE Transactions on Automatic Control, 21(1), 3–13.
- Zameer, A., Naz, S., and Raja, M.A.Z. (2024). Parallel differential evolution paradigm for multilayer electromechanical device optimization. Modern Physics Letters B, 2450312.
- Zhang, S.X., Hu, X.R., and Zheng, S.Y. (2024). Differential evolution with evolutionary scale adaptation. Swarm and Evolutionary Computation, 85, 101481.
- Ziegler, J.G. and Nichols, N.B. (1942). Optimum settings for automatic controllers. Transactions of the American society of mechanical engineers, 64(8), 759–765.