Abstract
As to the control problem of industrial processes with various constraints, the desired control performance may not be obtained by the employment of conventional control strategies, such as PID control (Tyreus and Luyben in Ind Eng Chem Res 31:2625–2628, 1992, [1]; Skogestad in J Process Control 13:291–309, 2003, [2]; Padula and Visioli in J Process Control 21:69–81, 2011, [3]; Lee at al in AIChE J 44:106–115, 1998, [4]).
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References
Tyreus, B. D., & Luyben, W. L. (1992). Tuning PI controllers for integrator/dead time processes. Industrial & Engineering Chemistry Research, 31(11), 2625–2628.
Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 13(4), 291–309.
Padula, F., & Visioli, A. (2011). Tuning rules for optimal PID and fractional-order PID controllers. Journal of Process Control, 21(1), 69–81.
Lee, Y., Park, S., Lee, M., & Brosilow, C. (1998). PID controller tuning for desired closed-loop responses for SI/SO systems. AIChE Journal, 44(1), 106–115.
Qin, S. J., & Badgwell, T. A. (2003). A survey of industrial model predictive control technology. Control Engineering Practice, 11(7), 733–764.
Lawrynczuk, M. (2017). Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation. ISA Transactions, 67, 476–495.
Lee, J. H. (2011). Model predictive control: Review of the three decades of development. International Journal of Control, Automation and Systems, 9(3), 415–424.
Lu, X. L., Kiumarsi, B., Chai, T. Y., & Lewis, F. L. (2016). Data-driven optimal control of operational indices for a class of industrial processes. IET Control Theory and Applications, 10(12), 1348–1356.
Wang, T., Gao, H. J., & Qiu, J. B. (2016). A combined fault-tolerant and predictive control for network-based industrial processes. IEEE Transactions on Industrial Electronics, 63(4), 2529–2536.
Bindlish, R. (2015). Nonlinear model predictive control of an industrial polymerization process. Computers & Chemical Engineering, 73, 43–48.
Huyck, B., Brabanter, J., Moor, B., Van Impe, J. F., & Logist, F. (2014). Online model predictive control of industrial processes using low level control hardware: A pilot-scale distillation column case study. Control Engineering Practice, 28, 34–48.
Mayne, D. Q. (2014). Model predictive control: Recent developments and future promise. Automatica, 50(12), 2967–2986.
Gallego, A. J., & Camacho, E. F. (2012). Adaptative state-space model predictive control of a parabolic-trough field. Control Engineering Practice, 20(9), 904–911.
Falugi, P., Olaru, S., & Dumur, D. (2010). Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law. International Journal of Control, 83(8), 1548–1563.
Miranda, H., Cortes, P., Yuz, J. I., & Rodriguez, J. (2009). Predictive torque control of induction machines based on state-space models. IEEE Transactions on Industrial Electronics, 56(6), 1916–1924.
Simkoff, J. M., Wang, S. Y., Baldea, M., Chiang, L. H., Castillo, I., Bindlish, R., et al. (2018). Plant-model mismatch estimation from closed-loop data for state-space model predictive control. Industrial & Engineering Chemistry Research, 57(10), 3732–3741.
Zou, T., Wu, S., & Zhang, R. D. (2018). Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization. ISA Transactions, 73, 147–153.
Wang, Y., & Boyd, S. (2010). Fast model predictive control using online optimization. IEEE Transactions on Control Systems Technology, 18(2), 267–278.
Kim, I., Chan, R., & Kwak, S. (2017). Model predictive control method for CHB multi-level inverter with reduced calculation complexity and fast dynamics. IET Electric Power Applications, 11(5), 784–792.
Huang, R., Biegler, L. T., & Patwardhan, S. C. (2010). Fast offset-free nonlinear model predictive control based on moving horizon estimation. Industrial & Engineering Chemistry Research, 49(17), 7882–7890.
Zheng, Y., Li, S. Y., & Tan, R. M. (2018). Distributed model predictive control for on-connected microgrid power management. IEEE Transactions on Control Systems Technology, 26(3), 1028–1039.
Velarde, P., Maestre, J. M., Ishii, H., & Negenborn, R. R. (2018). Vulnerabilities in Lagrange-based distributed model predictive control. Optimal Control Applications & Methods, 39(2), 601–621.
Long, Y. S., Liu, S., Xie, L. H., & Johansson, K. H. (2018). Distributed nonlinear model predictive control based on contraction theory. International Journal of Robust and Nonlinear Control, 28(2), 492–503.
Franze, G., Lucia, W., & Tedesco, F. (2018). A distributed model predictive control scheme for leader-follower multi-agent systems. International Journal of Control, 91(2), 369–382.
Pourkargar, D. B., Almansoori, A., & Daoutidis, P. (2017). Impact of decomposition on distributed model predictive control: A process network case study. Industrial & Engineering Chemistry Research, 56(34), 9606–9616.
Tarisciotti, L., Lo Calzo, G., Gaeta, A., Zanchetta, P., Valencia, F., & Saez, D. (2016). A distributed model predictive control strategy for back-to-back converters. IEEE Transactions on Industrial Electronics, 63(9), 5867–5878.
Kersbergen, B., van den Boom, T., & Schutter, B. (2016). Distributed model predictive control for railway traffic management. Transportation Research Part C-Emerging Technologies, 68, 462–489.
Farina, M., Ferrari, G. P., Manenti, F., & Pizzi, E. (2016). Assessment and comparison of distributed model predictive control schemes: Application to a natural gas refrigeration plant. Computers & Chemical Engineering, 89, 192–203.
Esfahani, N. R., & Khorasani, K. (2016). A distributed model predictive control (MPC) fault reconfiguration strategy for formation flying satellites. International Journal of Control, 89(5), 960–983.
Halvgaard, R., Vandenberghe, L., Poulsen, N. K., Madsen, H., & Jorgensen, J. B. (2016). Distributed model predictive control for smart energy systems. IEEE Transactions on Smart Grid, 7(3), 1675–1682.
Farhadi, A., & Khodabandehlou, A. (2016). Distributed model predictive control with hierarchical architecture for communication: Application in automated irrigation channels. International Journal of Control, 89(8), 1725–1741.
Gao, Y. L., Xia, Y. Q., & Dai, L. (2015). Cooperative distributed model predictive control of multiple coupled linear systems. IET Control Theory and Applications, 9(17), 2561–2567.
Li, H. P., & Shi, Y. (2014). Robust distributed model predictive control of constrained continuous-time nonlinear systems: A robustness constraint approach. IEEE Transactions on Automatic Control, 59(6), 1673–1678.
Stewart, B. T., Wright, S. J., & Rawlings, J. B. (2011). Cooperative distributed model predictive control for nonlinear systems. Journal of Process Control, 21(5), 698–704.
Razavinasab, Z., Farsangi, M. M., & Barkhordari, M. (2017). State estimation-based distributed model predictive control of large-scale networked systems with communication delays. IET Control Theory and Applications, 11(15), 2497–2505.
Jalal, R. E., & Rasmussen, B. P. (2017). Limited-communication distributed model predictive control for coupled and constrained subsystems. IEEE Transactions on Control Systems Technology, 25(5), 1807–1815.
Giselsson, P., Doan, M. D., Keviczky, T., Schutter, B., & Rantzer, A. (2013). Accelerated gradient methods and dual decomposition in distributed model predictive control. Automatica, 49(3), 829–833.
Zhuge, J. J., & Ierapetritou, M. G. (2015). An integrated framework for scheduling and control using fast model predictive control. AIChE Journal, 61(10), 3304–3319.
Li, S. E., Jia, Z. H., Li, K. Q., & Cheng, B. (2015). Fast online computation of a model predictive controller and its application to fuel economy-oriented adaptive cruise control. IEEE Transactions on Intelligent Transportation Systems, 16(3), 1199–1209.
Richards, A. (2015). Fast model predictive control with soft constraints. European Journal of Control, 25, 51–59.
Ahmed, H. (2015). Reactive power and voltage control in grid-connected wind farms: An online optimization based fast model predictive control approach. Electrical Engineering, 97(1), 35–44.
Jaschke, J., Yang, X., & Biegler, L. T. (2014). Fast economic model predictive control based on NLP-sensitivities. Journal of Process Control, 24(8), 1260–1272.
Lopez-Negrete, R., D’Amato, F. J., Biegler, L. T., & Kumar, A. (2013). Fast nonlinear model predictive control: Formulation and industrial process applications. Computers & Chemical Engineering, 51, 55–64.
Xu, F., Chen, H., Gong, X., & Mei, Q. (2016). Fast nonlinear model predictive control on FPGA using particle swarm optimization. IEEE Transactions on Industrial Electronics, 63(1), 310–321.
Zhang, Y. L., Wu, X. J., Yuan, X. B., Wang, Y. J., & Dai, P. (2016). Fast model predictive control for multilevel cascaded H-bridge STATCOM with polynomial computation time. IEEE Transactions on Industrial Electronics, 63(8), 5231–5243.
Nguyen, H. N., Bourdais, R., & Gutman, P. O. (2017). Fast model predictive control for linear periodic systems with state and control constraints. International Journal of Robust and Nonlinear Control, 27(17), 3703–3726.
Summers, S., Jones, C. N., Lygeros, J., & Morari, M. (2011). A multiresolution approximation method for fast explicit model predictive control. IEEE Transactions on Automatic Control, 56(11), 2530–2541.
Lin, S., Schutter, B., Xi, Y. G., & Hellendoorn, H. (2011). Fast model predictive control for urban road networks via MILP. IEEE Transactions on Intelligent Transportation Systems, 12(3), 846–856.
Wei, C. S., Luo, J. J., Dai, H. H., Yin, Z. Y., Ma, W. H., & Yuan, J. P. (2017). Globally robust explicit model predictive control of constrained systems exploiting SVM-based approximation. International Journal of Robust and Nonlinear Control, 27(16), 3000–3027.
Oberdieck, R., Diangelakis, N. A., & Pistikopoulos, E. N. (2017). Explicit model predictive control: A connected-graph approach. Automatica, 76, 103–112.
Chakrabarty, A., Dinh, V., Corless, M. J., Rundell, A. E., Zak, S. H., & Buzzard, G. T. (2017). Support vector machine informed explicit nonlinear model predictive control using low-discrepancy sequences. IEEE Transactions on Automatic Control, 62(1), 135–148.
Gao, Y., & Sun, L. N. (2016). Explicit solution of min-max model predictive control for uncertain systems. IET Control Theory and Applications, 10(4), 461–468.
Wallace, M., Kumar, S. S. P., & Mhaskar, P. (2016). Offset-free model predictive control with explicit performance specification. Industrial & Engineering Chemistry Research, 55(4), 995–1003.
Oberdieck, R., & Pistikopoulos, E. N. (2015). Explicit hybrid model-predictive control: The exact solution. Automatica, 58, 152–159.
Rivotti, P., & Pistikopoulos, E. N. (2015). A dynamic programming based approach for explicit model predictive control of hybrid systems. Computers & Chemical Engineering, 72, 126–144.
Hegrenaes, O., Gravdahl, J. T., & Tondel, P. (2005). Spacecraft attitude control using explicit model predictive control. Automatica, 41(12), 2107–2114.
Beccuti, A. G., Mariethoz, S., Cliquennois, S., Wang, S., & Morari, M. (2009). Explicit model predictive control of dc-dc switched-mode power supplies with extended Kalman filtering. IEEE Transactions on Industrial Electronics, 56(6), 1864–1874.
Charitopoulos, V. M., & Dua, V. (2016). Explicit model predictive control of hybrid systems and multiparametric mixed integer polynomial programming. AIChE Journal, 62(9), 3441–3460.
Chakrabarty, A., Buzzard, G. T., & Zak, S. H. (2017). Output-tracking quantized explicit nonlinear model predictive control using multiclass support vector machines. IEEE Transactions on Industrial Electronics, 64(5), 4130–4138.
Nascu, L., Oberdieck, R., & Pistikopoulos, E. N. (2017). Explicit hybrid model predictive control strategies for intravenous anaesthesia. Computers & Chemical Engineering, 106, 814–825.
Wang, F. X., Li, S. H., Mei, X. Z., Xie, W., Rodriguez, J., & Kennel, R. M. (2015). Model-based predictive direct control strategies for electrical drives: An experimental evaluation of PTC and PCC methods. IEEE Transactions on Industrial Informatics, 11(3), 671–681.
Muller, M. A., Angeli, D., & Allgower, F. (2015). On Necessity and robustness of dissipativity in economic model predictive control. IEEE Transactions on Automatic Control, 60(6), 1671–1676.
Zhang, Y. C., & Qu, C. Q. (2015). Model predictive direct power control of PWM rectifiers under unbalanced network conditions. IEEE Transactions on Industrial Electronics, 62(7), 4011–4022.
Ma, Y., & Cai, Y. L. (2018). A fuzzy model predictive control based upon adaptive neural network disturbance observer for a constrained hypersonic vehicle. IEEE Access, 6, 5927–5938.
Du, G. P., Liu, Z. F., Du, F., & Li, J. J. (2017). Performance improvement of model predictive control using control error compensation for power electronic converters based on the Lyapunov function. Journal of Power Electronics, 17(4), 983–990.
Nodozi, I., & Rahmani, M. (2017). LMI-based model predictive control for switched nonlinear systems. Journal of Process Control, 59, 49–58.
Ghaffari, V. (2017). A robust control system scheme based on model predictive controller (MPC) for continuous-time systems. Optimal Control Applications & Methods, 38(6), 1032–1041.
Song, Y., Fang, X. S., & Diao, Q. D. (2016). Mixed H2/H∞ distributed robust model predictive control for polytopic uncertain systems subject to actuator saturation and missing measurements. International Journal of Systems Science, 47(4), 777–790.
Tahir, F., & Jaimoukha, I. M. (2013). Causal state-feedback parameterizations in robust model predictive control. Automatica, 49(9), 2675–2682.
Ghaffari, V., Naghavi, S. V., & Safavi, A. A. (2013). Robust model predictive control of a class of uncertain nonlinear systems with application to typical CSTR problems. Journal of Process Control, 23(4), 493–499.
Mohammadkhani, M., Bayat, F., & Jalali, A. A. (2017). Robust output feedback model predictive control: A stochastic approach. Asian Journal of Control, 19(6), 2085–2096.
Ding, B. C., Xi, Y. G., & Li, S. S. (2004). A synthesis approach of on-line constrained robust model predictive control. Automatica, 40(1), 163–167.
Zhang, L. W., Xie, W., & Wang, J. C. (2017). Robust distributed model predictive control of linear systems with structured time-varying uncertainties. International Journal of Control, 90(11), 2449–2460.
Villanueva, M. E., Quirynen, R., Diehl, M., Chachuat, B., & Houska, B. (2017). Robust MPC via min-max differential inequalities. Automatica, 77, 311–321.
Liu, X. J., Jiang, D., & Lee, K. Y. (2015). Quasi-min-max fuzzy MPC of UTSG water level based on off-line invariant set. IEEE Transactions on Nuclear Science, 62(5), 2266–2272.
Ramirez, D. R., Alamo, T., & Camacho, E. F. (2011). Computational burden reduction in min-max MPC. Journal of The Franklin Institute-Engineering and Applied Mathematics, 348(9), 2430–2447.
Mhaskar, P. (2006). Robust model predictive control design for fault-tolerant control of process systems. Industrial & Engineering Chemistry Research, 45(25), 8565–8574.
Li, H. P., & Shi, Y. (2014). Event-triggered robust model predictive control of continuous-time nonlinear systems. Automatica, 50(5), 1507–1513.
Calafiore, G. C., & Fagiano, L. (2013). Robust model predictive control via scenario optimization. IEEE Transactions on Automatic Control, 58(1), 219–224.
Teng, L., Wang, Y. Y., Cai, W. J., & Li, H. (2017). Robust model predictive control of discrete nonlinear systems with time delays and disturbances via T-S fuzzy approach. Journal of Process Control, 53, 70–79.
Ojaghi, P., Bigdeli, N., & Rahmani, M. (2016). An LMI approach to robust model predictive control of nonlinear systems with state-dependent uncertainties. Journal of Process Control, 47, 1–10.
Brunner, F. D., Heemels, M., & Allgower, F. (2016). Robust self-triggered MPC for constrained linear systems: A tube-based approach. Automatica, 72, 73–83.
Ghasemi, M. S., & Afzalian, A. A. (2017). Robust tube-based MPC of constrained piecewise affine systems with bounded additive disturbances. Nonlinear Analysis-Hybrid Systems, 26, 86–100.
Bumroongsri, P., & Kheawhom, S. (2016). An off-line formulation of tube-based robust MPC using polyhedral invariant sets. Chemical Engineering Communications, 203(6), 736–745.
Bumroongsri, P. (2015). Tube-based robust MPC for linear time-varying systems with bounded disturbances. International Journal of Control, Automation and Systems, 13(3), 620–625.
Yan, Z., Le, X. Y., & Wang, J. (2016). Tube-based robust model predictive control of nonlineat systems via collective neurodynamic optimization. IEEE Transactions on Industrial Electronics, 63(7), 4377–4386.
Hariprasad, K., & Bhartiya, S. (2016). A computationally efficient robust tube based MPC for linear switched systems. Nonlinear Analysis-Hybrid Systems, 19, 60–76.
Mayne, D. Q., Kerrigan, E. C., van Wyk, E. J., & Falugi, P. (2011). Tube-based robust nonlinear model predictive control. International Journal of Robust and Nonlinear Control, 21(11), 1341–1353.
Farina, M., Giulioni, L., & Scattolini, R. (2016). Stochastic linear model predictive control with chance constraints—A review. Journal of Process Control, 44, 53–67.
Wang, Y., Ocampo-Martinez, C., & Puig, V. (2016). Stochastic model predictive control based on Gaussian processes applied to drinking water networks. IET Control Theory and Applications, 10(8), 947–955.
Putz, E., & Cipriano, A. (2015). Hybrid model predictive control for flotation plants. Minerals Engineering, 70, 26–35.
Sarailoo, M., Rahmani, Z., & Rezaie, B. (2014). Fuzzy predictive control of a boiler-turbine system based on a hybrid model system. Industrial & Engineering Chemistry Research, 53(6), 2362–2381.
Rubagotti, M., Barcelli, D., & Bemporad, A. (2014). Robust explicit model predictive control via regular piecewise-affine approximation. International Journal of Control, 87(12), 2583–2593.
Ong, C. J., Wang, Z. M., & Dehghan, M. (2016). Model predictive control for switching systems with dwell-time restriction. IEEE Transactions on Automatic Control, 61(12), 4189–4195.
Xu, W. D., Zhang, J. F., & Zhang, R. D. (2017). Application of multi-model switching predictive functional control on the temperature system of an electric heating furnace. ISA Transactions, 68, 287–292.
Hariprasad, K., & Bhartiya, S. (2017). An efficient and stabilizing model predictive control of switched system. IEEE Transactions on Automatic Control, 62(7), 3401–3407.
Zhang, L. X., Zhuang, S. L., & Braatz, R. D. (2016). Switched model predictive control of switched linear systems: Feasibility, stability and robustness. Automatica, 67, 8–21.
Khanmirza, E., Esmaeilzadeh, A., & Markazi, A. H. D. (2016). Predictive control of a building hybrid heating system for energy cost reduction. Applied Soft Computing, 46, 407–423.
Yu, K. J., Yang, H. Z., Tan, X. G., Kawabe, T., Guo, Y. N., Liang, Q., et al. (2016). Model predictive control for hybrid electric vehicle platooning using slope information. IEEE Transactions on Intelligent Transportation Systems, 17(7), 1894–1909.
Zhang, K., Sprinkle, J., & Sanfelice, R. G. (2016). Computationally aware switching criteria for hybrid model predictive control of cyber-physical systems. IEEE Transactions on Automation Science and Engineering, 13(2), 479–490.
Zhao, J. F., & Wang, J. M. (2016). Integrated model predictive control of hybrid electric vehicles coupled with aftertreatment systems. IEEE Transactions on Vehicular Technology, 65(3), 1199–1211.
Lopez-Sanz, J., Ocampo-Martinez, C., Alvarez-Florez, J., Moreno-Eguilaz, M., Ruiz-Mansilla, R., Kalmus, J., et al. (2017). Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 66(5), 3632–3644.
Johansen, T. A. (2017). Toward dependable embedded model predictive control. IEEE Systems Journal, 11(2), 1208–1219.
Necoara, I. (2015). Computational complexity certification for dual gradient method: Application to embedded MPC. Systems & Control Letters, 81, 49–56.
Lucia, S., Navarro, D., Lucia, O., Zometa, P., & Findeisen, R. (2018). Optimized FPGA implementation of model predictive control for embedded systems using high-level synthesis tool. IEEE Transactions on Industrial Informatics, 14(1), 137–145.
Kufoalor, D. K. M., Imsland, L., & Johansen, T. A. (2016). Efficient implementation of step response models for embedded model predictive control. Computers & Chemical Engineering, 90, 121–135.
Takacs, G., Batista, G., Gulan, M., & Rohal’-llkiv, B. (2016). Embedded explicit model predictive vibration control. Mechatronics, 36, 54–62.
Alanqar, A., Durand, H., & Christofides, P. D. (2017). Fault-tolerant economic model predictive control using error-triggered online model identification. Industrial & Engineering Chemistry Research, 56(19), 5652–5667.
Broomhead, T., Manzie, C., Hield, P., Shekhar, R., & Brear, M. (2017). Economic model predictive control and applications for diesel generators. IEEE Transactions on Control Systems Technology, 25(2), 388–400.
Wang, Y., Puig, V., & Cembrano, G. (2017). Non-linear economic model predictive control of water distribution networks. Journal of Process Control, 56, 23–34.
Olanrewaju, O. I., & Maciejowski, J. M. (2017). Implications of dissipativity on stability of economic model predictive control the indefinite linear quadratic case. Systems & Control Letters, 100, 43–50.
Maestre, J. M., Fernandez, M. I., & Jurado, T. (2018). An application of economic model predictive control to inventory management in hospitals. Control Engineering Practice, 71, 120–128.
Deng, K., Sun, Y., Li, S. S., Lu, Y., Brouwer, J., Mehta, P. G., et al. (2015). Model predictive control of central chiller plant with thermal energy storage via dynamic programming and mixed-integer linear programming. IEEE Transactions on Automation Science and Engineering, 12(2), 565–579.
Vichik, S., & Borrelli, F. (2014). Solving linear and quadratic programs with an analog circuit. Computers & Chemical Engineering, 70, 160–171.
Jones, C. N., Grieder, P., & Rakovic, S. V. (2006). A logarithmic-time solution to the point location problem for parametric linear programming. Automatica, 42(12), 2215–2218.
Ke, F., Li, Z. J., Xiao, H. Z., & Zhang, X. B. (2017). Visual servoing of constrained mobile robots based on model predictive control. IEEE Transactions on Systems Man Cybernetics:Systems, 47(7), 1428–1438.
Harrison, C. A., & Qin, S. J. (2009). Minimum variance performance map for constrained model predictive control. Journal of Process Control, 19(7), 1199–1204.
Baker, R., & Swartz, C. L. E. (2008). Interior point solution of multilevel quadratic programming problems in constrained model predictive control applications. Industrial & Engineering Chemistry Research, 47(1), 81–91.
Avanzini, G. B., Zanchettin, A. M., & Rocco, P. (2018). Constrained model predictive control for mobile robotic manipulators. Robotica, 36(1), 19–38.
Zeilinger, M. N., Morari, M., & Jones, C. N. (2014). Soft constrained model predictive control with robust stability guarantees. IEEE Transactions on Automatic Control, 59(5), 1190–1202.
Lamburn, D. J., Gibbens, P. W., & Dumble, S. J. (2014). Efficient constrained model predictive control. European Journal of Control, 20(6), 301–311.
Tarczewski, T., & Grzesiak, L. M. (2016). Constrained state feedback speed control of PMSM based on model predictive approach. IEEE Transactions on Industrial Electronics, 63(6), 3867–3875.
Liu, K. L., Li, K., & Zhang, C. (2017). Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model. Journal of Power Sources, 347, 145–158.
Zhang, R. D., Zou, Q., Cao, Z. X., & Gao, F. R. (2017). Design of fractional order modeling based extended non-minimal state space MPC for temperature in an industrial electric heating furnace. Journal of Process Control, 56, 13–22.
Zhang, R. D., Lu, R. Q., & Xue, A. K. (2014). Predictive functional control for linear systems under partial actuator faults and application on an injection molding batch process. Industrial & Engineering Chemistry Research, 53(2), 723–731.
Zhang, R. D., Xue, A. K., Wang, S. Q., & Ren, Z. Y. (2011). An improved model predictive control approach based on extended non-minimal state space formulation. Journal of Process Control, 21(8), 1183–1192.
Zhang, R. D., Gao, F. R., & Christofides, P. D. (2017). An improved approach for H∞ design of linear quadratic tracking control for chemical processes with partial actuator failure. Journal of Process Control, 58, 63–72.
Zhang, R. D., Xue, A. K., Wang, S. Q., & Zhang, J. M. (2012). An improved state space model structure and a corresponding predictive functional control design with improved control performance. International Journal of Control, 85(8), 1146–1161.
Zhang, R. D., Wu, S., Lu, R. Q., & Gao, F. R. (2014). Predictive control optimization based PID control for temperature in an industrial surfactant reactor. Chemometrics and Intelligent Laboratory Systems, 135(15), 48–62.
Zhang, R. D., Cao, Z. X., Bo, C. M., Li, P., & Gao, F. R. (2014). New PID controller design using extended non-minimal state space model based predictive functional control structure. Industrial & Engineering Chemistry Research, 53(8), 3283–3292.
Wu, S. (2015). State space predictive functional control optimization based new PID design for multivariable processes. Chemometrics and Intelligent Laboratory Systems, 143(15), 16–27.
Wu, S. (2015). Multivariable PID control using improved state space model predictive control optimization. Industrial & Engineering Chemistry Research, 54(20), 5505–5513.
Zhang, R. D., Zou, H. B., Xue, A. K., & Gao, F. R. (2014). GA based predictive functional control for batch processes under actuator faults. Chemometrics and Intelligent Laboratory Systems, 137(15), 67–73.
Zhang, R. D., Xue, A. K., & Gao, F. R. (2014). Temperature control of industrial coke furnace using novel state space model predictive control. IEEE Transactions on Industrial Informatics, 10(4), 2084–2092.
Zhang, R. D., Xue, A. K., Lu, R. Q., Li, P., & Gao, F. R. (2014). Real-time implementation of improved state-space MPC for air supply in a coke furnace. IEEE Transactions on Industrial Electronics, 61(7), 3532–3539.
Wang, L. P., & Young, P. C. (2006). An improved structure for model predictive control using non-minimal state space realisation. Journal of Process Control, 16(4), 355–371.
Rivera, D., Morari, M., & Skogestad, S. (1986). Internal model control: PID controller design. Industrial & engineering chemistry process design and development, 25, 252–265.
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Zhang, R., Xue, A., Gao, F. (2019). Introduction. In: Model Predictive Control. Springer, Singapore. https://doi.org/10.1007/978-981-13-0083-7_1
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