References
- Arahal, M., Berenguel, M. and Camacho, E. (1998). Neural identification applied to predictive control of a solar plant, Control Engineering Practice6(3): 333–344.
- Bismor, D. (2015). Extension of LMS stability condition over a wide set of signals, International Journal of Adaptive Control and Signal Processing29(5): 653–670, DOI: 10.1002/acs.2500.
- Camacho, E. and Bordons, C. (1999). Model Predictive Control, Springer, London.
- Colin, G., Chamaillard, Y., Bloch, G. and Corde, G. (2007). Neural control of fast nonlinear systems application to a turbocharged SI engine with VCT, IEEE Transactions on Neural Networks18(4): 1101–1114.
- de Oliveira, N. and Biegler, L. (1995). An extension of Newton-type algorithms for nonlinear process control, Automatica52(2): 281–286.
- Deng, J., Becerra, V.M. and Stobart, R. (2009). Input constraints handling in an MPC/feedback linearization scheme, International Journal of Applied Mathematics and Computer Science19(2): 219–232, DOI: 10.2478/v10006-009-0018-2.
- Doyle, F., Ogunnaike, B. and Pearson, R. (1995). Nonlinear model-based control using second-order Volterra models, Automatica31(5): 697–714.
- Ellis, M., Durand, H. and Christofides, P. (2014). A tutorial review of economic model predictive control methods, Journal of Process Control24(8): 1156–1178.
- Gonzalez, A., Adam, E. and Marchetti, J. (2008). Conditions for offset elimination in state space receding horizon controllers: A tutorial analysis, Chemical Engineering and Processing47(12): 2184–2194.
- Kuure-Kinsey, M., Cutright, R. and Bequette, B. (2006). Computationally efficient neural predictive control based on a feedforward architecture, Industrial and Engineering Chemistry Research45(25): 8575–8582.
- Ławryńczuk, M. (2007). A family of model predictive control algorithms with artificial neural networks, International Journal of Applied Mathematics and Computer Science17(2): 217–232, DOI: 10.2478/v10006-007-0020-5.
- Ławryńczuk, M. (2014). Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach, Studies in Systems, Decision and Control, Vol. 3, Springer, Heidelberg.
- Lee, J. and Ricker, N. (1994). Extended Kalman filter based nonlinear model predictive control, Industrial and Engineering Chemistry Research33(6): 1530–1541.
- Maciejowski, J. (2002). Predictive Control with Constraints, Prentice Hall, Harlow.
- Maeder, U. and Morari, M. (2010). Offset-free reference tracking with model predictive control, Automatica46(9): 1469–1476.
- Mayne, D. (2014). Model predictive control: Recent developments and future promise, Automatica50(12): 2967–2986.
- Megías, D., Serrano, J. and Ghoumari, M.E. (1999). Extended linearised predictive control: Practical control algorithms for non-linear systems, Proceedings of the European Control Conference, ECC 1999, Karlsruhe, Germany, F883.
- Mu, J., Rees, D. and Liu, G. (2005). Advanced controller design for aircraft gas turbine engines, Journal of Process Control13(8): 1001–1015.
- Muske, K. and Badgwell, T. (2002). Disturbance modeling for offset-free linear model predictive control, Journal of Process Control12(5): 617–632.
- Pannocchia, G. and Bemporad, A. (2007). Combined design of disturbance model and observer for offset-free model predictive control, IEEE Transactions on Automatic Control52(6): 1048–1053.
- Pannocchia, G. and Rawlings, J. (2003). Disturbance models for offset-free model predictive control, AIChE Journal49(2): 426–437.
- Patan, K. and Korbicz, J. (2012). Nonlinear model predictive control of a boiler unit: A fault tolerant control study, International Journal of Applied Mathematics and Computer Science22(1): 225–237, DOI: 10.2478/v10006-012-0017-6.
- Qin, S. and Badgwell, T. (2003). A survey of industrial model predictive control technology, Control Engineering Practice11(7): 733–764.
- Rawlings, J. and Mayne, D. (2009). Model Predictive Control: Theory and Design, Nob Hill Publishing, Madison.
- Simon, D. (2006). Optimal State Estimation: Kalman, H∞and Nonlinear Approaches, John Wiley & Sons, Hoboken, NJ.
- Tatjewski, P. (2007). Advanced Control of Industrial Processes, Structures and Algorithms, Springer, London.
- Tatjewski, P. (2010). Supervisory predictive control and on-line set-point optimization, International Journal of Applied Mathematics and Computer Science20(3): 483–495, DOI: 10.2478/v10006-010-0035-1.
- Tatjewski, P. (2014). Disturbance modeling and state estimation for offset-free predictive control with state-space models, International Journal of Applied Mathematics and Computer Science24(2): 313–323, DOI: 10.2478/amcs-2014-0023.
- Tatjewski, P. and Ławryńczuk, M. (2006). Soft computing in model-based predictive control, International Journal of Applied Mathematics and Computer Science16(1): 7–26.
- Wang, L. (2007). Model Predictive Control System Design and Implementation Using MATLAB, Springer, London.