Takagi Sugeno type Fuzzy Modeling and Predictive Control of a Level Plant
DOI:
https://doi.org/10.71701/541q0m10Keywords:
Takagi Sugeno Fuzzy Modeling, Model-Based Discrete Predictive Control, Level Plant, Programmable Logic ControllerAbstract
This research develops the modeling of a level plant using fuzzy inference systems of Takagi Sugeno type, as well as its control using a Discrete Model Predictive Control (DMPC). In the modeling process, the usefulness of Takagi Sugeno fuzzy inference systems has been demonstrated when constructing models reproducing the nonlinear behavior of plants in a wide range of operation. The sub-models of the process were obtained by estimating parameters using an estimator of maximum verisimilitude. Then, based on this information, a Discrete Model Predictive Control (DMPC) was designed, and afterwards, the tuning was made with Simulink®. The practical implementation was carried out using a LogiX family Allen Bradley® controller, in which the necessary functions were programmed to implement the DMPC. The experimental results show a superior performance of this strategy compared to a classic PID controller in terms of reduction of settling time, maximum overshoot, better filtering of noise in the control signal, as well as a uniform behavior over a wide range of operation.
Downloads
References
Ansari, A. T., Raja, K. T., Sujitha, K., Kaa, H., y Abirami, S. (2014). Assessment Of Diverse Controllers For A Cylindrical Tank Level Process. International Journal for Innovative Research in Science & Technology, 1 (6), 82-86.
Åström, K., y Hägglund, T. (1995). PID Controllers: Theory, Design and Tuning. Carolina del Norte: Instrument Society of America.
Atherton, D. P., y Majhi, S. (Junio de 1999). Limitations of PID controllers. The 1999 American Control Conference. Conferencia llevada a cabo en IEEE Control Sistems Society, San Diego, California. 3843-3847.
Buckley, J. (1992). Universal fuzzy controllers, Automatica (Journal of IFAC), 28 (6), 1245–1248. [5] Camacho, E., y Alba, C. (2013). Model predictive control. Londres: Springer Science & Business Media.
Dotoli, M., Fay, A., Miskowicz, M., y Seatzu, C. (2015). A Survey on Advanced Control Approaches in Factory Automation. IFAC-PapersOnLine, 48 (3), 394-399.
Escobar, E. Salgado, J, Nicanor, Q. (octubre, 2014). Gain Scheduled - Model Predictive Control Applied to Nonlinear Systems, Memorias del XVI Congreso Latinoamericano de Control Automático (CLCA 2014). Congreso llevado a cabo en Cancún, Quintana Roo, México. 899-904.
Fini, A., Gogani, M., y Pourgholi, M. (Setiembre, 2015). Fuzzy gain scheduling of PID controller implemented on real time level control. Fuzzy and Intelligent Systems (CFIS), 2015 4th Iranian Joint Congress. Congreso llevado a cabo en IEEE, Zahedan, Iran. 1-5.
Qin, J., y Badgwell, T. (2003). A survey of industrial model predictive control technology. Control engineering practice, 11(7), 733-764.
Kamyar, M. (2008). Takagi-Sugeno Fuzzy Modeling for Process Control. Industrial Automation, Robotics and Artificial Intelligence. School of Electrical, Electronic and Computer Engineering, Newcastle University.
Lucas Nülle (s. f.): Compact level control kit including vessel, tank, pump and sensors. Recuperado de https://www.lucas-nuelle.com/317/pid/13909/apg/7474/Compact-level-control-kit-including-vessel,-tank,-pump-and-sensors--.htm
Marruedo, D. (2002). Control predictivo de sistemas no lineales con restricciones: estabilidad y robustez [Disertación doctoral], Universidad de Sevilla.
Muñoz-Tamayo, R., Laroche, B., Leclerc, M., & Walter, E. (2009). IDEAS: a parameter identification toolbox with symbolic analysis of uncertainty and its application to biological modelling. IFAC Proceedings Volumes, 42(10), 1271-1276.
Ricker, N. (1991). Model-predictive control: state of the art. Proc. Fourth International Conference on Chemical Process Control, Texas. 271–296.
Rivas Pérez, Sotomayor Moriano, Prada Moraga. (Noviembre, 2000). Control predictivo adaptivo robusto. IX Congreso Latinoamericano de Control Automático. Congreso llevado a cabo en Cali, Colombia. 10-15
Sbárbaro, D., y Del Villar, R. (Eds.). (2010). Advanced control and supervision of mineral processing plants. Londres: Springer Science & Business Media.
Sung, S. W., y Lee, I. B. (1996). Limitations and countermeasures of PID controllers. Industrial & engineering chemistry research, 35(8), 2596-2610.
Takagi, T., y Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control, IEEE transactions on systems, man, and cybernetics, 15(1), 116–132.
Viveros, R., Yuz, J., y Ibacache, R. (2014). Estimación simultánea de estado y parámetros para un sistema no lineal variante en el tiempo. Revista Iberoamericana de Automática e Informática industrial, 11(3), 263-274.
Wang, L. (2009). Model predictive control system design and implementation using MATLAB®. Londres: Springer Science & Business Media.
Wang, M., y Crusca, F. (2002). Design and implementation of a gain scheduling controller for a water level control system. ISA transactions, 41(3), 323-331.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
