Abstract
In applied research areas, various types of mathematical disciplines have been advantageously connected together with wide corresponding applications. As an applied proposal of this connection, a numerical optimization method of the quadratic programming particularly modified by a principle of statistical hypothesis testing can be seen in this paper. With regards to a computational complexity, algorithms of multivariable Model Predictive Control (MPC) can be considered as procedures with a higher computational complexity caused by the multi-variability, higher horizons and included constraints conditions. A wide spectrum of modifications has been proposed in the optimization subsystem of MPC controller yet; however, approaches based on including the hypotheses testing have not been widely considered in applied optimization method. A number of operations should be decreased; however, a control quality may be slightly influenced with regards to this aim. Therefore, the proposed modification is advantageous in an applied form of the quadratic programming technique where necessary information for following steps of a process control are provided. Achieved results are discussed in order to the incorporating of the principle of hypotheses testing in the modified numerical method of the applied quadratic programming.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Corriou, J.P.: Process Control: Theory and Applications. Springer, Heidelberg (2004)
Xu, S., Ni, D., Lu, S., et al.: A novel digital multi-mode control strategy with PSM for primary-side flyback converter. Int. J. Electron. 104(5), 840–854 (2017). https://doi.org/10.1080/00207217.2016.1253783. ISSN 0020-7217
Li, C., Mao, Y., Yang, J., et al.: A nonlinear generalized predictive control for pumped storage unit. Renew. Energy 114, 945–959 (2017). https://doi.org/10.1016/j.renene.2017.07.055. ISSN 0960-1481
Sun, D., Xu, S., Sun, W., et al.: A new digital predictive control strategy for boost PFC converter. IEICE Electron. Exp. 12(23), 9 (2015). https://doi.org/10.1587/elex.12.20150726. ISSN 1349-2543
Zheng, Y., Zhou, J., Zhu, W., et al.: Design of a multi-mode intelligent model predictive control strategy for hydroelectric generating unit. Neurocomputing 207, 287–299 (2016). https://doi.org/10.1016/j.neucom.2016.05.007. ISSN 0925-2312
Abraham, A., Pappa, N., Honc, D., et al.: Reduced order modelling and predictive control of multivariable nonlinear process. Sadhana – Acad. Proc. Eng. Sci. 43(3) (2018). https://doi.org/10.1007/s12046-018-0798-x. ISSN 0256-2499
Navratil, P., Pekar, L., Klapka, J.: Load distribution of heat source in production of heat and electricity. Int. Energy J. 17(3), 99–111 (2017). ISSN 1513-718X
Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, Heidelberg (2004)
Rossiter, J.A.: Model Based Predictive Control: A Practical Approach. CRC Press, Boca Raton (2003)
Kwon, W.H.: Receding Horizon Control: Model Predictive Control for State Models. Springer, Heidelberg (2005)
Kubalcik, M., Bobal, V., Barot, T.: Modified Hildreth’s method applied in multivariable model predictive control. In: Innovation, Engineering and Entrepreneurship. Lecture Notes in Electrical Engineering, vol. 505, pp. 75–81. Springer (2019). https://doi.org/10.1007/978-3-319-91334-6_11. ISBN 978-3-319-91333-9
Ingole, D., Holaza, J., Takacs, B., et al.: FPGA-based explicit model predictive control for closed loop control of intravenous anesthesia. In: 20th International Conference on Process Control (PC), pp. 42–47. IEEE (2015). https://doi.org/10.1109/pc.2015.7169936
Wang, L.: Model Predictive Control System Design and Implementation Using MATLAB. Springer, Heidelberg (2009)
Dostal, Z.: Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities. Springer, Heidelberg (2009)
Kitchenham, B., Madeyski, L., Budgen, D., et al.: Robust statistical methods for empirical software engineering. Empir. Softw. Eng. 22, 1–52 (2016)
Sulovska, K., Belaskova, S., Adamek, M.: Gait patterns for crime fighting: statistical evaluation. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 8901. SPIE (2013). https://doi.org/10.1117/12.2033323. ISBN 978-081949770-3
Pivarc, J.: Ideas of Czech primary school pupils about intellectual disability. Educ. Stud. Taylor & Francis (2018, in press). https://doi.org/10.1080/03055698.2018.1509784. ISSN 0305-5698
Navratil, P., Balate, J.: One of possible approaches to control of multivariable control loop. IFAC Proc. 40(5), 207–212 (2007). https://doi.org/10.3182/20070606-3-mx-2915.00033. ISSN 1474-6670
Alizadeh Noughabi, H.: Two powerful tests for normality. Ann. Data Sci. 3(2), 225–234 (2016). ISSN 2198-5812
Vaclavik, M., Sikorova, Z., Barot, T.: Particular analysis of normality of data in applied quantitative research. In: Computational and Statistical Methods in Intelligent Systems. Advances in Intelligent Systems and Computing, vol. 859, pp. 353–365. Springer (2019). https://doi.org/10.1007/978-3-030-00211-4_31. ISBN 978-3-319-91333-9
Hunger, R.: Floating point operations in matrix-vector calculus. (Version 1.3), Technical Report. Technische Universität München, Associate Institute for Signal Processing (2007)
Kubalcik, M., Bobal, V.: Adaptive control of coupled drives apparatus based on polynomial theory. Proc. IMechE Part I: J. Syst. Control Eng. 220(I7), 641–654 (2006). https://doi.org/10.1109/cca.2002.1040252
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Barot, T., Krpec, R., Kubalcik, M. (2019). Applied Quadratic Programming with Principles of Statistical Paired Tests. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-31362-3_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31361-6
Online ISBN: 978-3-030-31362-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)