Offset-free ARX-based adaptive model predictive control applied to a nonlinear process

ISA Transactions - Tập 123 - Trang 251-262 - 2022
Anthony Perez1, Yu Yang1
1Department of Chemical Engineering, California State University Long Beach, Long Beach, CA, 90840, USA

Tài liệu tham khảo

Qin, 2003, A survey of industrial model predictive control technology, Control Eng Pract, 11, 733, 10.1016/S0967-0661(02)00186-7 Hussain, 1999, Review of the applications of neural networks in chemical process control-simulation and online implementation, Artif Intell Eng, 13, 55, 10.1016/S0954-1810(98)00011-9 Darby, 2012, MPC: Current practice and challenges, Control Eng Pract, 20, 328, 10.1016/j.conengprac.2011.12.004 Hosen, 2011, Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation, Control Eng Pract, 19, 454, 10.1016/j.conengprac.2011.01.007 Peng, 2011, Multivariable RBF-ARX model-based robust MPC approach and application to thermal power plant, Appl Math Model, 35, 3541, 10.1016/j.apm.2011.01.002 Patan, 2015, Neural network-based model predictive control: Fault tolerance and stability, IEEE Trans Control Syst Technol, 23, 1147, 10.1109/TCST.2014.2354981 Seborg, 2016 Cutler C, Ramaker B. Dynamic matrix control-a computer control algorithm. In: Proceedings of the joint automatic control conference. 1980. Clarke, 1987, Generalized predictive control - part i. The basic algorithm, Automatica, 23, 137, 10.1016/0005-1098(87)90087-2 Rawlings, 1993, Stability of constrained receding horizon control, IEEE Trans Automat Control, 38, 1512, 10.1109/9.241565 Kalra, 1994, Effect of process nonlinearity on the performance of linear model predictive controllers for the environmentally safe operation of a fluid catalytic cracking unit, Ind Eng Chem Res, 49, 3063, 10.1021/ie00036a022 Fukushima, 2007, Adaptive model predictive control for a class of constrained linear systems based on the comparison model, Automatica, 43, 301, 10.1016/j.automatica.2006.08.026 Dougherty, 2003, A practical multiple model adaptive strategy for single-loop MPC, Control Eng Pract, 11, 141, 10.1016/S0967-0661(02)00106-5 Zhu, 2016, Adaptive model predictive control for unconstrained discrete-time linear systems with parametric uncertainties, IEEE Trans Automat Control, 61, 3171, 10.1109/TAC.2015.2505783 Tanaskovic M, Fagiano L, Smith R, Goulart P, Morari M. Adaptive model predictive control for constrained linear systems. In: Proceedings of European control conference. 2013. p. 382–7. Karra, 2008, Adaptive model predictive control of multivariable time-varying systems, Ind Eng Chem Res, 47, 2708, 10.1021/ie070823y Chan, 2014, Predictive control with adaptive model maintenance: Application to power plants, Comput Chem Eng, 70, 91, 10.1016/j.compchemeng.2014.03.011 Abdelwahed, 2017, Adaptive MPC based on MIMO ARX-Laguerre model, ISA Trans, 67, 330, 10.1016/j.isatra.2016.11.017 Zhu, 2013, Toward a low cost and high performance MPC: The role of system identification, Comput Chem Eng, 51, 124, 10.1016/j.compchemeng.2012.07.005 Chikasha, 2017, Adaptive model predictive control of a quadrotor, IFAC-PapersOnLine, 50, 157, 10.1016/j.ifacol.2017.12.029 Rawlings, 2000, Tutorial overview of model predictive control, IEEE Control Syst Mag, 20, 38, 10.1109/37.845037 Pannocchia G. Offset-free tracking MPC: A tutorial review and comparison of different formulations. In: Proceedings of European control conference. 2015. p. 527–32. Rao, 1998, Application of interior point methods to model predictive control, J Optim Theory Appl, 99, 723, 10.1023/A:1021711402723 Richalet, 1978, Model predictive heuristic control: Applications to industrial processes, Automatica, 14, 413, 10.1016/0005-1098(78)90001-8 Muske, 2002, Disturbance modeling for offset free linear model predictive control, J Process Control, 12, 617, 10.1016/S0959-1524(01)00051-8 Pannocchia, 2003, Disturbance models for offset-free model-predictive control, AIChE J, 49, 426, 10.1002/aic.690490213 Rajamani, 2009, Achieving state estimation equivalence for misassigned disturbances in offset-free model predictive control, AIChE J, 55, 396, 10.1002/aic.11673 Pannocchia, 2007, Combined design of disturbance model and observer for offset-free model predictive control, IEEE Trans Automat Control, 52, 1048, 10.1109/TAC.2007.899096 Huusom JK, Poulsen NK, Jørgensen SB, Jørgensen JB. Tuning of methods for offset free MPC based on ARX model representations. In: Proceedings of American control conference. 2010. p. 2355–60. Yang, 2019, Economic model predictive control for achieving offset-free operationperformance of industrial constrained systems, J Process Control, 80, 103, 10.1016/j.jprocont.2019.04.006 Simkoff, 2018, Plant-model mismatch estimation from closed-loop data for state-space model predictive control, Ind Eng Chem Res, 57, 3732, 10.1021/acs.iecr.7b04917 Muske, 1993, Model predictive control with linear models, AIChE J, 39, 262, 10.1002/aic.690390208 Žáčeková, 2013, Persistent excitation condition within the dual control framework, J Process Control, 23, 1270, 10.1016/j.jprocont.2013.08.004 Ljung, 1999 Henson M, Seborg D. Nonlinear control strategies for continuous fermenters. In: Proceedings of American control conference. San Diego (CA, USA); 1990. p. 2723–28. Wojsznis, 2003, Practical approach to tuning MPC, ISA Trans, 42, 149, 10.1016/S0019-0578(07)60121-9 Garriga, 2010, Model predictive control tuning methods: A review, Ind Eng Chem Res, 49, 3505, 10.1021/ie900323c Achterberg, 2009, SCIP: Solving constraint integer programs, Math Program Comput, 1, 1, 10.1007/s12532-008-0001-1 Currie, 2012