The DOI matches its Crossref record; 124 of the 127 references it cites were found in a registry or an index.
Constrained model predictive control: Stability and optimality
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Model predictive control (MPC) [12], or receding horizon control [13], is an optimization-based control addressing optimal tracking control performance and input and state constraints, such as saturation of control inputs and physical limits of workspace and speed.”
From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [12]
Citations
Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Model predictive control: Theory and practice—A survey
García, C. E., Prett, D. M., & Morari, M. (1989) · Automatica · doi:10.1016/0005-1098(89)90002-2 · Cited by 5,392
Lee, J. H., Kaisare, N. S., & García, C. E. (2007) · Springer eBooks · doi:10.1002/9781394333325.fmatter · Cited by 3,766
Generalized predictive control - Part I. The basic algorithm
Clarke, D., Mohtadi, C., & Tuffs, P. (1987) · at - Automatisierungstechnik · doi:10.1016/0005-1098(87)90087-2 · Cited by 3,712
Introduction to Stochastic Control Theory
(1970) · Mathematics in Science and Engineering/Mathematics in science and engineering · doi:10.1016/s0076-5392(09)x6015-7 · Cited by 3,205
Control of systems integrating logic, dynamics, and constraints
Bemporad, A., & Morari, M. (1999) · Automatica · doi:10.1016/s0005-1098(98)00178-2 · Cited by 3,048
Deterministic and Stochastic Optimal Control
Fleming, W. H., & Rishel, R. (1975) · Springer eBooks · doi:10.1007/978-1-4612-6380-7 · Cited by 2,747
Deterministic and Stochastic Optimal Control.
Bather, J. A., Fleming, W. H., & Rishel, R. (1976) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344363 · Cited by 2,690
Robust constrained model predictive control using linear matrix inequalities
Kothare, M. V., Balakrishnan, V., & Morari, M. (1996) · Automatica · doi:10.1016/0005-1098(96)00063-5 · Cited by 2,521
Model predictive control: past, present and future
Morari, M., & H. Lee, J. (1999) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(98)00301-9 · Cited by 2,335
Research works printed but not linked: 13
- Allwright, J. C., (1993). On min-max model-based predictive control. Proceedings Oxford symposium on advances in model based predictive control, Oxford (pp. 4153–426).
- Camacho, E., & Bordons, C. (1998). Model predictive control. Berlin: Springer.
- De Nicolao, G., Magni, L., & Scattolini, R. (1996c). Stabilizing nonlinear receding horizon control via a nonquadratic penalty. Proceedings of the IMACS multiconference CESA, vol. 1, Lille, France (pp. 185–187).
- Gauthier, 1983, Commande multivariable en présence de constraintes de type inégalité, Revue d'Automatique d'Informatique et de Recherche Opérationnelle (RAIRO), 17, 205
- Kantor, J. C., Garcı́a, C. E., & Carnahan, B. (1997). (Eds.), Fifth international conference on chemical process control, CACHE, A.I.Ch.E.
- Magni, M., De Nicolao, G., Scattolini, R., & Allgöwer, F. (1999a). H∞ receding horizon control for non-linear discrete-time systems. Systems & Control Letters, submitted. · DOI
- Mayne, D. Q. (1997). Nonlinear model predictive control: An assessment. In: J. C. Kantor, C. E. Garcı́a, & B. Carnahan (Eds.), Fifth International Conference on Chemical Process Control, CACHE, A.I.Ch.E. (pp. 217–231).
- Mosca, E. (1994). Optimal, predictive and adaptive control, Information and system science Series. Englewoods Cliffs, NJ: Prentice Hall.
- Qin, S. J., & Badgwell, T. A. (1997). An overview of industrial model predictive control technology. In: J. C. Kantor, C. E. Garcı́a, & B. Carnahan (Eds.), Fifth International Conference on Chemical Process Control, CACHE, AIChE, (pp. 232–256).
- Rao, C. V., & Rawlings, J. B. (1998). Nonlinear moving horizon estimation. Preprints of international symposium on nonlinear model predictive control: Assessment and future directions, Monte Verità, Ascona, Switzerland (pp. 146–163).
- Richalet, J., Rault, A., Testud, J. L., & Papon, J. (1976). Algorithmic control of industrial processes. Proceedings of the Fourth IFAC symposium on identification and system parameter estimation (pp. 1119–1167).
- Robertson, D., & Lee, J. (1996). Statistical interpretation of constrained moving horizon estimation. Proceedings of A.I.Ch.E annual meeting, Chicago, IL.
- Wright, S. J. (1997). Applying new optimization algorithms to model predictive control. In: J.C. Kantor, C.E. Garcı́a, B. Carnahan (Eds.), Fifth international conference on chemical process control, CACHE, AIChE (pp. 147–155).
Cited by
Sorted by citations
Items 601–700 of 1,004
Model predictive control of constrained piecewise affine discrete‐time systems
Mayne, D. Q., & Raković, S. V. (2003) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.817 · Cited by 95
Self-citationOn hybrid systems and closed-loop MPC systems
Bemporad, A., Heemels, W. P. M. H., & Schutter, B. D. (2002) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2002.1000287 · Cited by 95
Model predictive dynamic control allocation with actuator dynamics
Luo, Y., Serrani, A., Yurkovich, S., Doman, D., & Oppenheimer, M. W. (2004) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2004.1386823 · Cited by 95
Congestion-controlled best-effort communication for networks-on-chip
van den Brand, J. W., Ciordas, C., Goossens, K. G. W., & Basten, T. (2007) · TU/e Research Portal · doi:10.5555/1266366.1266571 · Cited by 95
Nonlinear Model Predictive Formation Flight
Shin, J., & Kim, H. J. (2009) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2009.2021935 · Cited by 95
Predicting microbial nitrate reduction pathways in coastal sediments
Algar, C. K., & Vallino, J. J. (2013) · Aquatic Microbial Ecology · doi:10.3354/ame01678 · Cited by 96
A Hybrid Control Scheme for Three-Phase Vienna Rectifiers
Li, X., Sun, Y., Wang, H., Su, M., & Huang, S. (2017) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2017.2661382 · Cited by 95
Li, D., Li, J., & Li, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.075 · Cited by 94
Economic Nonlinear Model Predictive Control
Faulwasser, T., Grüne, L., & Müller, M. A. (2018) · now publishers, Inc. eBooks · doi:10.1561/9781680833935 · Cited by 94
Learning-Based Predictive Control for Discrete-Time Nonlinear Systems With Stochastic Disturbances
Xu, X., Chen, H., Lian, C., & Li, D. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2820019 · Cited by 94
A Sub-Optimal Second Order Sliding Mode Controller for Systems With Saturating Actuators
Ferrara, A., & Rubagotti, M. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.2010992 · Cited by 94
A survey of numerical algorithms for trajectory optimization of flight vehicles
Huang, G., Lu, Y., & Nan, Y. (2012) · Science China Technological Sciences · doi:10.1007/s11431-012-4946-y · Cited by 98
Albi, G., Choi, Y.-P., Fornasier, M., & Dante Kalise, B. (2017) · Applied Mathematics & Optimization · doi:10.1007/s00245-017-9429-x · Cited by 91
Zhang, J., Sun, T., & Liu, Z. (2017) · Ocean Engineering · doi:10.1016/j.oceaneng.2017.07.057 · Cited by 94
Explicit Model Predictive Control Approach for Low-Thrust Spacecraft Proximity Operations
Leomanni, M., Rogers, E., & Gabriel, S. B. (2014) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g000477 · Cited by 93
Sensitivity-based coordination in distributed model predictive control
Scheu, H., & Marquardt, W. (2011) · Journal of Process Control · doi:10.1016/j.jprocont.2011.01.013 · Cited by 93
Fast economic model predictive control based on NLP-sensitivities
Jäschke, J., Xue, Y., & Biegler, L. T. (2014) · Journal of Process Control · doi:10.1016/j.jprocont.2014.04.009 · Cited by 93
Moving horizon closed‐loop production scheduling using dynamic process models
Pattison, R. C., Touretzky, C. R., Harjunkoski, I., & Bâldea, M. (2016) · AIChE Journal · doi:10.1002/aic.15408 · Cited by 93
On the Choice of Norm in Finite Control Set Model Predictive Control
Καραμανάκος, Π., Geyer, T., & Kennel, R. M. (2017) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2017.2756092 · Cited by 93
Virtual Coupling in Railways: A Comprehensive Review
Mindán, J. F., & Vaquero-Serrano, M. A. (2023) · Machines · doi:10.3390/machines11050521 · Cited by 93
An improved robust model predictive control design in the presence of actuator saturation
Huang, H., Li, D., Lin, Z., & Xi, Y. (2011) · Automatica · doi:10.1016/j.automatica.2011.01.045 · Cited by 93
On the design and tuning of linear model predictive control for wind turbines
Jain, A., Schildbach, G., Fagiano, L., & Morari, M. (2015) · Renewable Energy · doi:10.1016/j.renene.2015.02.057 · Cited by 93
Model Predictive Control for Quasilinear Hyperbolic Distributed Parameter Systems
Shang, H., Forbes, J. F., & Guay, M. (2004) · Industrial & Engineering Chemistry Research · doi:10.1021/ie030653z · Cited by 93
Körber, A., & King, R. (2013) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2013.2260749 · Cited by 93
Predictive Control Using an FPGA With Application to Aircraft Control
Hartley, E. N., Jerez, J. L., Suardi, A., Maciejowski, J. M., Kerrigan, E. C., & Constantinides, G. A. (2013) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2013.2271791 · Cited by 92
Saska, M., Vonásek, V., Krajník, T., & Přeučil, L. (2014) · The International Journal of Robotics Research · doi:10.1177/0278364914530482 · Cited by 92
Wang, J., Gong, S., Peeta, S., & Lu, L. (2019) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2019.08.002 · Cited by 92
Extended robust model predictive control
Odloak, D. (2004) · AIChE Journal · doi:10.1002/aic.10175 · Cited by 92
Model predictive control of switched nonlinear systems under average dwell-time
Müller, M. A., Martius, P., & Allgöwer, F. (2012) · Journal of Process Control · doi:10.1016/j.jprocont.2012.07.004 · Cited by 92
Robust Tubes in Nonlinear Model Predictive Control
Cannon, M., Buerger, J., Kouvaritakis, B., & Raković, S. V. (2011) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2135190 · Cited by 92
Advances in sensitivity-based nonlinear model predictive control and dynamic real-time optimization
Biegler, L. T., Yang, X., & Fischer, G. A. G. (2015) · Journal of Process Control · doi:10.1016/j.jprocont.2015.02.001 · Cited by 93
Zong, Y., Böning, G. M., Santos, R. M., You, S., Hu, J., & Han, X. (2016) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2016.11.141 · Cited by 91
Safe Exploration and Optimization of Constrained MDPs Using Gaussian Processes
Wachi, A., Sui, Y., Yue, Y., & Ono, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12103 · Cited by 91
Economic model predictive control with self-tuning terminal cost
Müller, M. A., Angeli, D., & Allgöwer, F. (2013) · European Journal of Control · doi:10.1016/j.ejcon.2013.05.019 · Cited by 91
Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization
Chatzilygeroudis, K. I., Cully, A., Vassiliades, V., & Mouret, J.-B. (2021) · Springer optimization and its applications · doi:10.1007/978-3-030-66515-9_4 · Cited by 101
Quadratic programming algorithms for large-scale model predictive control
Bartlett, R. A., Biegler, L. T., Backström, J. U., & Gopal, V. (2002) · Journal of Process Control · doi:10.1016/s0959-1524(02)00002-1 · Cited by 91
On-line nonlinear model predictive control of a PEM fuel cell system
Ziogou, C., Papadopoulou, S., Georgiadis, M. C., & Voutetakis, S. S. (2013) · Journal of Process Control · doi:10.1016/j.jprocont.2013.01.011 · Cited by 91
ADVANCED CONTROL AND ON-LINE PROCESS OPTIMIZATION IN MULTILAYER STRUCTURES
Tatjewski, P. (2007) · IFAC Proceedings Volumes · doi:10.3182/20070723-3-pl-2917.00004 · Cited by 91
Zengin, U., & Dogan, A. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2006.889490 · Cited by 91
Collective behavior coordination with predictive mechanisms
Zhang, H., Chen, M. J., Stan, G.-B. V., Zhou, T., & Maciejowski, J. M. (2008) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2008.928446 · Cited by 90
Dynamic optimization of the Tennessee Eastman process using the OptControlCentre
Jockenhövel, T., Biegler, L. T., & Wächter, A. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(03)00113-3 · Cited by 91
Li, S. E., Guo, Q., Xu, S., Duan, J., Li, S., Li, C., & Su, K. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2736246 · Cited by 91
Robust MPC of constrained nonlinear systems based on interval arithmetic
Limón, D., Bravo, J. M., Álamo, T., & Camacho, E. F. (2005) · IEE Proceedings - Control Theory and Applications · doi:10.1049/ip-cta:20040480 · Cited by 89
Robust MPC Tower Damping for Variable Speed Wind Turbines
Evans, M. A., Cannon, M., & Kouvaritakis, B. (2014) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2014.2310513 · Cited by 90
Dufour, P., Touré, Y., Blanc, D., & Laurent, P. (2003) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(03)00099-1 · Cited by 90
Tube-based robust economic model predictive control
Bayer, F. A., Müller, M. A., & Allgöwer, F. (2014) · Journal of Process Control · doi:10.1016/j.jprocont.2014.06.006 · Cited by 90
Moving horizon H∞ control with performance adaptation for constrained linear systems
Chen, H., & Scherer, C. W. (2006) · Automatica · doi:10.1016/j.automatica.2006.03.001 · Cited by 90
Sequential Action Control: Closed-Form Optimal Control for Nonlinear and Nonsmooth Systems
Ansari, A. R., & Murphey, T. D. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2596768 · Cited by 90
Liu, J., Chen, X., de la Peña, D. M., & Christofides, P. D. (2011) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2164729 · Cited by 89
Oops! I cannot do it again: Testing for recursive feasibility in MPC
Löfberg, J. (2012) · Automatica · doi:10.1016/j.automatica.2011.12.003 · Cited by 89
Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback
Zhou, J., Tian, D., Sheng, Z., Duan, X., Qu, G., Zhao, D., Cao, D., & Shen, X. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3146149 · Cited by 89
“To directly and explicitly incorporate the control objective and constraints of a plant in its control law design, a promising way is to join constrained optimization theory with modern control theory [45].”
MethodAlternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving
Ma, J., Cheng, Z., Zhang, X., Tomizuka, M., & Lee, T. H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3194571 · Cited by 89
Ficchì, A., Raso, L., Dorchies, D., Pianosi, F., Malaterre, P., van Overloop, P. J., & Jay‐Allemand, M. (2015) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0000571 · Cited by 89
Kraus, T., Ferreau, H. J., Kayacan, E., Ramón, H., Baerdemaeker, J. D., Diehl, M., & Saeys, W. (2013) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2013.06.009 · Cited by 89
Power Kites for Wind Energy Generation [Applications of Control]
Canale, M., Fagiano, L., & Milanese, M. (2007) · IEEE Control Systems · doi:10.1109/mcs.2007.909465 · Cited by 89
Optimal Control and Scheduling of Switched Systems
Görges, D., Izak, M., & Liu, S. (2010) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2010.2085573 · Cited by 89
A Multiresolution Approximation Method for Fast Explicit Model Predictive Control
Summers, S., Jones, C. N., Lygeros, J., & Morari, M. (2011) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2146990 · Cited by 89
Quasi-min–max MPC for constrained nonlinear systems with guaranteed input-to-state stability
He, D., Huang, H., & Chen, Q. (2014) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2014.03.006 · Cited by 88
Huang, J., Cao, Y., Xiong, C., & Zhang, H. (2018) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2018.2867939 · Cited by 87
High precision control and deep learning-based corn stand counting algorithms for agricultural robot
Zhang, Z., Kayacan, E., Thompson, B., & Chowdhary, G. V. (2020) · Autonomous Robots · doi:10.1007/s10514-020-09915-y · Cited by 88
Cao, Y., & Huang, J. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003351 · Cited by 88
“Model predictive control (MPC) has been widely recognized from academia and industry due to its conceptual simplicity and ability to effectively handle complex systems with hard control constraints and multiple inputs and outputs [17]–[19].”
BackgroundMixed-integer programming in motion planning
Ioan, D., Prodan, I., Olaru, S., Stoican, F., & Niculescu, S. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.10.008 · Cited by 89
“For this reason and due to the increasing interest in optimization-based control (Mayne et al., 2000), MIP has became an essential technique, which allows to include logical decisions and non-convex constraints in the optimization problem.”
BackgroundReinforcement learning-based NMPC for tracking control of ASVs: Theory and experiments
Martinsen, A. B., Lekkas, A. M., & Gros, S. (2021) · Control Engineering Practice · doi:10.1016/j.conengprac.2021.105024 · Cited by 88
A Nonlinear Model Predictive Control for the Position Tracking of Cable-Driven Parallel Robots
Santos, J. C., Gouttefarde, M., & Chemori, A. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3152705 · Cited by 88
Adaptative state-space model predictive control of a parabolic-trough field
Gallego, A. J., & Camacho, E. F. (2012) · Control Engineering Practice · doi:10.1016/j.conengprac.2012.05.010 · Cited by 88
Robust constrained predictive control of uncertain norm-bounded linear systems
Casavola, A., Famularo, D., & Franzè, G. (2004) · Automatica · doi:10.1016/j.automatica.2004.05.016 · Cited by 88
Model Predictive Control for Reference Tracking on an Industrial Machine Tool Servo Drive
Stephens, M. A., Manzie, C. G., & Good, M. C. (2012) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2223222 · Cited by 88
Bioengineering Solutions for Manufacturing Challenges in CAR T Cells
Piscopo, N. J., Mueller, K. P., Das, A., Hematti, P., Murphy, W. L., Palecek, S. P., Capitini, C. M., & Saha, K. (2017) · Biotechnology Journal · doi:10.1002/biot.201700095 · Cited by 93
Huang, X., Song, Y., & Lai, J. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2821668 · Cited by 87
Yang, H., Deng, F., He, Y., Jiao, D., & Han, Z. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107885 · Cited by 87
A Tube-MPC Approach to Autonomous Multi-Vehicle Racing on High-Speed Ovals
Wischnewski, A., Herrmann, T., Werner, F., & Lohmann, B. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3169986 · Cited by 87
“It is well known [25] that the application of this nominal MPC scheme (see [21] for a thorough discussion of constrained”
BackgroundOffline motion libraries and online MPC for advanced mobility skills
Bjelonic, M., Grandia, R., Geilinger, M., Harley, O., Medeiros, V. S., Pajovic, V., Jelavić, E., Coros, S., & Hutter, M. (2022) · The International Journal of Robotics Research · doi:10.1177/02783649221102473 · Cited by 87
“Instead, a large amount of pioneering work in model predictive control (MPC) (see reviews by Morari and Lee (1999); Mayne et al. (2000); Bertsekas (2005); Diehl et al. (2009)) focuses on the online execution of whole-body optimization problems on the real robot in a task-generic fashion (Caron and…”
BackgroundOn the sample size of random convex programs with structured dependence on the uncertainty
Zhang, X., Grammatico, S., Schildbach, G., Goulart, P. J., & Lygeros, J. (2015) · Automatica · doi:10.1016/j.automatica.2015.07.013 · Cited by 87
Min-max model predictive control of nonlinear systems using discontinuous feedbacks
Fontes, F. A. C. C., & Magni, L. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.817915 · Cited by 87
Model Predictive Control for a Full Bridge DC/DC Converter
Xie, Y., Ghaemi, R., Sun, J., & Freudenberg, J. S. (2011) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2011.2107575 · Cited by 87
Self-triggered MPC for constrained linear systems and quadratic costs
Barradas-Berglind, J. J., Gommans, T. M. P., & Heemels, W. P. M. H. (2012) · IFAC Proceedings Volumes · doi:10.3182/20120823-5-nl-3013.00058 · Cited by 87
Explicit stochastic predictive control of combustion plants based on Gaussian process models
Grancharova, A., Kocijan, J., & Johansen, T. A. (2008) · Automatica · doi:10.1016/j.automatica.2008.04.002 · Cited by 87
On distributed constrained formation control in operator–vehicle adversarial networks
Zhu, M., & Martı́nez, S. (2013) · Automatica · doi:10.1016/j.automatica.2013.09.031 · Cited by 87
Short-term optimal operation of water systems using ensemble forecasts
Raso, L., Schwanenberg, D., van de Giesen, N., & van Overloop, P. J. (2014) · Advances in Water Resources · doi:10.1016/j.advwatres.2014.06.009 · Cited by 87
Resource-aware MPC for constrained nonlinear systems: A self-triggered control approach
Gommans, T. M. P., & Heemels, W. P. M. H. (2015) · Systems & Control Letters · doi:10.1016/j.sysconle.2015.03.003 · Cited by 86
Game Theoretic Model Predictive Control for Distributed Energy Demand-Side Management
Stephens, E. R., Smith, D. B., & Mahanti, A. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2014.2377292 · Cited by 86
Probabilistic tubes in linear stochastic model predictive control
Cannon, M., Kouvaritakis, B., & Ng, D. (2009) · Systems & Control Letters · doi:10.1016/j.sysconle.2009.08.004 · Cited by 86
Gilbert, E. G., & Ong, C. (2010) · Automatica · doi:10.1016/j.automatica.2010.10.016 · Cited by 86
Offset‐free receding horizon control of constrained linear systems
Pannocchia, G., & Kerrigan, E. C. (2005) · AIChE Journal · doi:10.1002/aic.10626 · Cited by 86
Qi, W. M., Liu, J., & Christofides, P. D. (2011) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2011.2119318 · Cited by 85
Robust stability of economically oriented infinite horizon NMPC that include cyclic processes
Huang, R., Biegler, L. T., & Harinath, E. (2011) · Journal of Process Control · doi:10.1016/j.jprocont.2011.10.010 · Cited by 85
Process Systems Engineering: Academic and industrial perspectives
Grossmann, I. E., & Harjunkoski, I. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.04.028 · Cited by 89
Hung, N. T., Rego, F., Quintas, J. M., Cruz, J., Jacinto, M., Souto, D., Potes, A., Sebastião, L., & Pascoal, A. (2022) · Journal of Field Robotics · doi:10.1002/rob.22142 · Cited by 85
Safe Trajectory Planning of Autonomous Vehicles
Schouwenaars, T. (2006) · Cited by 85
Model Based Control of Refrigeration Systems
Larsen, L. F. S. (2006) · Cited by 85
Packetized Predictive Control over Erasure Channels
Quevedo, D. E., Silva, E. I., & Goodwin, G. C. (2007) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2007.4282630 · Cited by 85
Fault‐tolerant control of nonlinear process systems subject to sensor faults
Mhaskar, P., Gani, A., McFall, C. W., Christofides, P. D., & Davis, J. F. (2007) · AIChE Journal · doi:10.1002/aic.11100 · Cited by 85
Alessandretti, A., Aguiar, A. P., & Jones, C. N. (2013) · European Control Conference (ECC) · doi:10.23919/ecc.2013.6669717 · Cited by 85
Zhu, B., & Xia, X. (2015) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2015.2505783 · Cited by 85
Linear Model Predictive Control Stability and Robustness
Löfberg, J. (2001) · Cited by 85
Tracking control of a marine surface vessel with full-state constraints
Yin, Z. J., He, W., & Yang, C. (2016) · International Journal of Systems Science · doi:10.1080/00207721.2016.1193255 · Cited by 84
A hierarchical approach for splitting truck platoons near network discontinuities
Duret, A., Wang, M., & Ladino, A. (2019) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2019.04.006 · Cited by 84
Distributed model predictive control of constrained nonlinear systems with communication delays
Li, H., & Shi, Y. (2013) · Systems & Control Letters · doi:10.1016/j.sysconle.2013.05.012 · Cited by 84
ON APPLICABILITY OF OPTIMAL CONTROL THEORY TO ADAPTIVE SUPPLY CHAIN PLANNING AND SCHEDULING
Ivanov, D., Dolgui, A. B., & Sokolov, B. (2011) · IFAC Proceedings Volumes · doi:10.3182/20110828-6-it-1002.01243 · Cited by 84
Nilsson, J., & Sjoberg, J. S. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629638 · Cited by 83
Metadata
- Authors
- Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. M.
- Affiliations
- Imperial College London (GB)
- University of Wisconsin–Madison (US)
- Orange (France) (FR)
- Corresponding author
- Mayne, D.Q.
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 2000-06
- Volume
- 36
- Issue
- 6
- Pages
- 789-814
- ISSN
- 0005-1098 (ISSN-L)
- OpenAlex
- W1978956894
- Cited by
- 8,736
- References
- 127
- Citations by year
- 2026: 406 · 2025: 372 · 2024: 336 · 2023: 400 · 2022: 386 · 2021: 424 · 2020: 487 · 2019: 448 · 2018: 426 · 2017: 404 · 2016: 412 · 2015: 400 · 2014: 455 · 2013: 397 · 2012: 380
- FWCI
- 231.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Automatica (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Keywords
- constrained model predictive control, stability analysis, optimality, finite-horizon optimal control, infinite horizon optimal control, nonlinear model predictive control, linear model predictive control, hard constraints, petrochemical process control, nonlinear control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review