Abstract
Predictive Controller of a laboratory thermal process is presented in the paper. Process model is approximated by a neural network. On-line optimization is done by a genetic algorithm. Control algorithm is tested on the laboratory thermal process and compared to the standard control methods like predictive controller with the transfer and state-space linear model and the quadratic programming optimization method or a PI controller.
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Rossiter, J.A.: Model-based Predictive Control – A Practical Approach. CRC Press, Boca Raton (2003)
Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, Heidelberg (2007)
Maciejowski, J.: Predictive Control with Constraints. Prentice Hall, Upper Saddle River (2002)
Allgöwer, F., Findeisen, R., Nagy, Z.K.: Nonlinear model predictive control: from theory to application. J. Chin. Inst. Chem. Engrs. 35(3), 299–315 (2004)
Magni, L., Raimondo, D.M., Allgöwer, F.: Nonlinear Model Predictive Control - Towards New Challenging Applications. Lecture Notes in Control and Information Sciences, vol. 384. Springer, Heidelberg (2009)
Qin, S.J., Badgwell, T.A.: An overview of nonlinear model predictive control applications, In: Nonlinear Predictive Control, pp. 369–392. Springer, Heidelberg (2000)
Honc, D., Doležel, P., Gago, L.: Predictive control of nonlinear plant using piecewise-linear neural model, In.: Proceedings of the 21st International Conference on Process Control, pp. 161–166 (2017)
Onnen, C., Babuška, R., Kaymak, U., Sousa, J.M., Verbruggen, H.B., Isermann, R.: Genetic algorithms for optimization in predictive control. Control Eng. Pract. 5(10), 1363–1372 (1997)
Chen, W., Li, X., Chen, M.: Suboptimal nonlinear model predictive control based on genetic algorithm, In.: Third International Symposium on Intelligent Information Technology Application Workshops, IITAW 2009, pp. 119–124. IEEE (2009)
Blasco, X., Martinez, M., Senent, J., Sanchis, J.: Generalized predictive control using genetic algorithms (GAGPC). An application to control of a non-linear process with model uncertainty. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 428–437, Springer, Heidelberg (1998)
Stojanovski, G., Stankovski, M.: Model predictive controller employing genetic algorithm optimization of thermal processes with non-convex constraints. In: Genetic Algorithms in Applications. InTech (2012)
Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K., Nahavandi, S.: Optimizing model predictive control horizons using genetic algorithm for motion cueing algorithm. Expert Syst. Appl. 92, 73–81 (2018)
Bobál, V.: Digital self-tuning Controllers: Algorithms, Implementation and Applications. Springer, London (2005)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1994)
Korbicz, J., Janczak, A.: A neural network approach to identification of structural systems. In: Proceedings of IEEE International Symposium on Industrial Electronics, Poland, pp. 98–103, vol. 1 (1996)
Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Machine Learn. 3(2), 95–99 (1988)
RT 040 Training system: temperature control, HIS. https://www.gunt.de/en/products/mechatronics/automation-and-process-control-engineering/simple-process-engineering-control-systems/training-system-temperature-control-hsi/080.04000/rt040/glct-1:pa-148:ca-83:pr-1045. Accessed 12 Jan 2019
U12 Series. https://labjack.com/products/u12.Accessed 12 Jan 2019
Honc, D., Sharma, K.R., Abraham, A., Dušek, F., Pappa, N.: Teaching and practicing model predictive control. IFAC-PapersOnLine 49(6), 34–39 (2016)
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This research was supported by Institutional support of The Ministry of Education, Youth and Sports of the Czech Republic at University of Pardubice and SGS grant at Faculty of Electrical Engineering and Informatics.
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Honc, D., Doležel, P., Merta, J. (2019). Thermal Process Control Using Neural Model and Genetic Algorithm. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Intelligent Systems Applications in Software Engineering. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1046. Springer, Cham. https://doi.org/10.1007/978-3-030-30329-7_35
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DOI: https://doi.org/10.1007/978-3-030-30329-7_35
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