Neural network based explicit MPC for chemical reactor control

Bakará č P, Kvasnica M (2018) Fast nonlinear model predictive control of a chemical reactor: a random shooting approach. Acta Chimica Slovaca, 11(2): 175—181.10.2478/acs-2018-0025Search in Google Scholar

Bakošová M, Mészáros A, Klemeš J, Oravec J (2012) Robust and optimal control approach for exothermic reactor stabilization. Theoretical Foundations of Chemical Engineering, (46): 740—746.10.1134/S0040579512060036Search in Google Scholar

Bakošová M, Oravec J, Mészáros A, Vasičkaninová A (2017) Neural-Network-Based and Robust Model-Based Predictive Control of a Tubular Heat Exchanger. Chemical Engineering Transactions, (61): 301—306.Search in Google Scholar

Bakošová M, Oravec J (2014) Robust mpc of an unstable chemical reactor using the nominal system optimization. Acta Chimica Slovaca, 7(2): 87—93.10.2478/acs-2014-0015Search in Google Scholar

Bemporad A, Morari M, Dua V, Pistikopoulos EN (2002) The explicit linear quadratic regulator for constrained systems. Automatica, 38(1): 3—20.10.1016/S0005-1098(01)00174-1Search in Google Scholar

Borrelli F, Bemporad A, Morari M (2017) Predictive Control for Linear and Hybrid Systems. Cambridge University Press.10.1017/9781139061759Search in Google Scholar

Fissore D (2008) Robust control in presence of parametric uncertainties: observer-based feedback controller design. Chemical Engineering Science, 63(7): 1890—1900.10.1016/j.ces.2007.12.019Search in Google Scholar

Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2): 251—257.10.1016/0893-6080(91)90009-TSearch in Google Scholar

Karg B, Lucia S (2018) Efficient representation and approximation of model predictive control laws via deep learning.Search in Google Scholar

Klaučo M, Kvasnica M (2019) MPC-Based Reference Governors. Springer, 1^st edition.10.1007/978-3-030-17405-7_1Search in Google Scholar

Lofberg J (2004) YALMIP: A Toolbox for Modeling and Optimization in MATLAB. In Proc. of the CACSD Conference, Taipei, Taiwan. Available from http://users.isy.liu.se/johanl/yalmip/.Search in Google Scholar

Lohr Y, Klaučo M, Kalúz M, Monnigmann M (2019) Mimicking predictive control with neural networks in domestic heating systems. In Fikar M and Kvasnica M, editors, Proceedings of the 22^nd International Conference on Process Control, pages 19—24, Šrbské Pleso, Slovakia. Slovak University of Technology in Bratislava, Slovak Chemical Library.10.1109/PC.2019.8815030Search in Google Scholar

Maciejowski JM (2002) Predictive Control with Constraints. PEARSON Prentice-Hall.Search in Google Scholar

Mayne DQ, Rawlings JB, Rao CV, Scokaert POM (2000) Constrained model predictive control: Stability and optimality. Automatica, 36(6): 789—814.10.1016/S0005-1098(99)00214-9Search in Google Scholar

Pourdehi S, Karimaghaee P (2018) Stability analysis and design of model predictive reset control for nonlinear time-delay systems with application to a two-stage chemical reactor system. Journal of Process Control, 71: 103—115.10.1016/j.jprocont.2018.09.010Search in Google Scholar

Prasath G, Recke B, Chidambaram M, Jørgensen J (2010) Application of Soft Constrained MPC to a Cement Mill Circuit. In Proceedings of the 9^th International Symposium on Dynamics and Control of Process Systems, Belgium, Leuven.10.3182/20100705-3-BE-2011.00050Search in Google Scholar

Singh A, de Villiers P, Rambalee P, Gous G, de Klerk J, Humphries G (2010) A holistic approach to the application of model predictive control to batch reactors. IFAC Proceedings Volumes, 43(9): 127—132. 13^th IFAC Symposium on Automation in Mining, Mineral and Metal Processing.10.3182/20100802-3-ZA-2014.00030Search in Google Scholar

Smets IY, Claes JE, November EJ, Bastin GP, Impe JFV (2004) Optimal adaptive control of (bio)chemical reactors: past, present and future. Journal of Process Control, 14(7): 795—805. Dynamics, Monitoring, Control and Optimization of Biological Systems.10.1016/j.jprocont.2003.12.005Search in Google Scholar