Identificação e implementação de um Controle Preditivo em um Sistema Térmico
DOI:
https://doi.org/10.33448/rsd-v12i1.39862Keywords:
Thermal system; Systems identification; Predictive control; PID control; Industrial didactic plant.Abstract
The present work consists of carrying out the identification and implementation of a predictive controller in a thermal system to follow a didactic design methodology for learning in systems identification, analysis, and controller design. For the identification of the thermal system, the method of least squares was chosen because it allows to obtain a faithful mathematical model that describes the temperature system. The data used in the system identification step are from a didactic industrial plant of a temperature system. From the model obtained for the system in question, the design and implementation of a classic Proportional Integral Derivative (PID) controller and a predictive controller are carried out to verify the behavior of the system against the actions of both controllers. The entire implementation will be carried out in the MATLAB/Simulink computer simulation environment.
References
Aguirre, L. A. (2004). Introdução à identificação de sistemas–Técnicas lineares e não-lineares aplicadas a sistemas reais. Editora UFMG.
Arnold, F., & King, R. (2021). State–space modeling for control based on physics-informed neural networks. Engineering Applications of Artificial Intelligence, 101, 104195.
Åström, K. J., & Hägglund, T. (2004). Revisiting the Ziegler–Nichols step response method for PID control. Journal of Process Control, 14(6), 635-650.
Camacho, E. F., & Alba, C. B. (2013). Model predictive control. Springer science & business media.
de Moraes, A. T., Rossi, F. Q., Galvão, R. K. H., & Kienitz, K. H. (2013). Controle Preditivo com Restrições para Controle de Nível do Tanque de uma Planta-Piloto Industrial. In. Anais do XI Simpósio Brasileiro de Automação Inteligente, Fortaleza – CE, Brasil.
Dorf, R. C., & Bishop, R. H. (2001). Sistemas de Controle Moderno. LTC.
Jaluria, Y. (2007). Design and optimization of thermal systems. CRC press.
Kaya, I. (2004). IMC based automatic tuning method for PID controllers in a Smith predictor configuration. Computers & Chemical Engineering, 28(3), 281-290.
Köche, J. C. (2016). Fundamentos de metodologia científica. Editora Vozes.
Nelles, O. (2020). Nonlinear system identification: from classical approaches to neural networks, fuzzy models, and gaussian processes. Springer Nature.
Nise, N. S. (2002). Engenharia de sistemas de controle. LTC.
Ogata, K. (2011). Engenharia de controle moderno. Prentice Hall.
Oliveira, M. D. M., Silva, R. C. M., & de Souza, D. L. (2020). Otimização multiobjetivo no projeto de controlador de nível em planta piloto. Research, Society and Development, 9(7), e743974794-e743974794.
Özerdem, Ö. C. (2016). Design of two experimental setups for programmable logic controller (PLC) laboratory. International Journal of Electrical Engineering Education, 53(4), 331-340.
Pinho, A. G., Olímpio, E. J. S., Cabral, L. M., de Oliveira Filho, R. M., Silva, B. C. R., Furriel, G. P. & de Melo Junior, G. (2021). Desenvolvimento de bancada didática contendo múltiplos sensores e atuadores. Research, Society and Development, 10(13), e222101321165-e222101321165.
Pugliese, L. F., de Oliveira, T. G., da Silva, D. L. F., Rodor, F. F., da Silva Braga, R. A., & Amorim, G. F. (2022). Modeling and development of a low-cost didactic plant for teaching in multivariable systems. Research, Society and Development, 11(7), e33011730249-e33011730249.
Qin, S. J., & Badgwell, T. A. (2003). A survey of industrial model predictive control technology. Control Engineering Practice, 11(7), 733-764.
Santos, L. S., de Moraes, M. N., Lopes, J. D. S., Bauer, L. C., Bonomo, P., & Bonomo, R. C. F. (2020). Modeling thermal properties of exotic fruits pulps: an artificial neural networks approach. Research, Society and Development, 9(11), e7509119806-e7509119806.
Santos, T. L. M. (2011). Contribuições para o controle preditivo com compensação de atraso robusta. Universidade Federal de Santa Catarina.
Shridhar, R., & Cooper, D. J. (1997). A tuning strategy for unconstrained SISO model predictive control. Industrial & Engineering Chemistry Research, 36(3), 729-746.
Tripp, D. (2005). Action research: a methodological introduction. Educação e pesquisa, 31(3), 443-466.
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Copyright (c) 2023 Luiz Felipe Pugliese; Tiago Gaiba de Oliveira; Fadul Ferrari Rodor; Rodrigo Aparecido da Silva Braga; Diogo Leonardo Ferreira da Silva
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