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 901–1,000 of 1,004
Sliding mode predictive control: A survey
Xiao, H., Zhao, D., Gao, S., & Spurgeon, S. K. (2022) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2022.07.003 · Cited by 67
Data Science and Model Predictive Control:
Morato, M. M., & Felix, M. S. (2024) · Journal of Process Control · doi:10.1016/j.jprocont.2024.103327 · Cited by 67
Relaxed Logarithmic Barrier Function Based Model Predictive Control of Linear Systems
Feller, C., & Ebenbauer, C. (2016) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2016.2582040 · Cited by 67
Simultaneous nonlinear model predictive control and state estimation
Copp, D. A., & Hespanha, J. P. (2017) · Automatica · doi:10.1016/j.automatica.2016.11.041 · Cited by 67
Congestion-Controlled Best-Effort Communication for Networks-on-Chip
van den Brand, J. W., Ciordas, C., Goossens, K. G. W., & Basten, T. (2007) · Design, Automation, and Test in Europe · doi:10.1109/date.2007.364415 · Cited by 67
Teng, L., Wang, Y., Cai, W., & Li, H. (2018) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2018.2815521 · Cited by 66
String Stable and Collision-Safe Model Predictive Platoon Control
Gratzer, A. L., Thormann, S., Schirrer, A., & Jakubek, S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3160236 · Cited by 66
Henze, B., Ott, C., & Roa, M. A. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6943014 · Cited by 66
INTEGRATED BRAKING AND STEERING MODEL PREDICTIVE CONTROL APPROACH IN AUTONOMOUS VEHICLES
Falcone, P., Tseng, H. E., Asgari, J., Borrelli, F., & Hrovat, D. (2007) · IFAC Proceedings Volumes · doi:10.3182/20070820-3-us-2918.00038 · Cited by 66
Gayaka, S., Lu, L., & Yao, B. (2012) · Automatica · doi:10.1016/j.automatica.2011.11.012 · Cited by 66
Ding, B., & Ping, X. (2012) · Journal of Process Control · doi:10.1016/j.jprocont.2012.07.011 · Cited by 66
Optimal charging profiles for mechanically constrained lithium-ion batteries
Suthar, B., Ramadesigan, V., De, S., Braatz, R. D., & Subramanian, V. R. (2013) · Physical Chemistry Chemical Physics · doi:10.1039/c3cp52806e · Cited by 66
Convex modeling of conflict resolution at traffic intersections
Murgovski, N., de Campos, G. R., & Sjoberg, J. S. (2015) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2015.7402953 · Cited by 66
A Computationally Efficient Model Predictive Control Strategy for Linear Systems With Integer Inputs
Καραμανάκος, Π., Geyer, T., & Kennel, R. M. (2015) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2015.2501348 · Cited by 66
On stochastic receding horizon control with bounded control inputs
Hokayem, P. F., Debasish Chatterjee, A., & Lygeros, J. (2009) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2009.5399951 · Cited by 66
Output Feedback Predictive Control of Interval Type-2 T–S Fuzzy Systems With Markovian Packet Loss
Tang, X., Deng, L., Yu, J.-M., & Qu, H. (2017) · IEEE transactions on fuzzy systems · doi:10.1109/tfuzz.2017.2771502 · Cited by 58
Model Predictive Control of Spacecraft Relative Motion with Convexified Keep-Out-Zone Constraints
Zagaris, C., Park, H., Virgili-Llop, J., Zappulla, R., Romano, M., & Kolmanovsky, I. V. (2018) · Journal of Guidance Control and Dynamics · doi:10.2514/1.g003549 · Cited by 66
Coordinated Voltage Control in Electric Power Systems
Larsson, M. R. (2001) · Lund University Publications (Lund University) · Cited by 66
Predictive cruise control with autonomous overtaking
Murgovski, N., & Sjoberg, J. S. (2015) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2015.7402302 · Cited by 65
RNN for Receding Horizon Control of Redundant Robot Manipulators
Yan, J., Jin, L., Yuan, Z., & Liu, Z. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2021.3062257 · Cited by 65
Single-Loop Robust Model Predictive Speed Regulation of PMSM Based on Exogenous Signal Preview
Liu, J., Yang, J., Li, S., & Wang, X. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3239938 · Cited by 65
On Stability and Performance of Stochastic Predictive Control Techniques
Debasish Chatterjee, A., & Lygeros, J. (2014) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2014.2335274 · Cited by 65
Özkan, L., & Kothare, M. V. (2005) · Journal of Process Control · doi:10.1016/j.jprocont.2005.06.013 · Cited by 65
Least-restrictive move-blocking model predictive control
Gondhalekar, R., & Imura, J. (2010) · Automatica · doi:10.1016/j.automatica.2010.04.010 · Cited by 65
Evolving Philosophies on Autonomous Obstacle/Collision Avoidance of Unmanned Aerial Vehicles
Mujumdar, A. P., & Padhi, R. (2010) · Journal of Aerospace Computing Information and Communication · doi:10.2514/1.49985 · Cited by 65
Distributed Estimation With Information-Seeking Control in Agent Networks
Meyer, F., Wymeersch, H., Fröhle, M., & Hlawatsch, F. (2015) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2015.2430519 · Cited by 65
Closed-loop belief space planning for linear, Gaussian systems
Vitus, M. P., & Tomlin, C. J. (2011) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2011.5980257 · Cited by 65
Input‐to‐state stabilizing sub‐optimal NMPC with an application to DC–DC converters
Lazar, M., Heemels, W. P. M. H., Roset, B. J. P., Nijmeijer, H., & van den Bosch, P. P. J. (2007) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.1249 · Cited by 65
Look-ahead control of heavy vehicles
Hellström, E. (2010) · Cited by 65
Qian, X., Fortelle, A. D. L., & Moutarde, F. (2016) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2016.7535413 · Cited by 63
Recent Applications of Advanced Control Techniques in Food Industry
Kondakci, T., & Zhou, W. (2016) · Food and Bioprocess Technology · doi:10.1007/s11947-016-1831-x · Cited by 65
Klaučo, M., Kalúz, M., & Kvasnica, M. (2017) · Control Engineering Practice · doi:10.1016/j.conengprac.2017.01.001 · Cited by 65
Model Predictive Approaches for Active Surge Control in Centrifugal Compressors
Torrisi, G., Grammatico, S., Cortinovis, A., Mercangöz, M., Morari, M., & Smith, R. S. (2017) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2016.2636027 · Cited by 65
Stability of finite horizon model predictive control with incremental input constraints
Yu, S., Qu, T., Xu, F., Chen, H., & Hu, Y. (2017) · Automatica · doi:10.1016/j.automatica.2017.01.040 · Cited by 65
Yan, W. M., Wang, Y., Ahn, C. K., & Duan, D. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.3021733 · Cited by 65
Predictive control of constrained nonlinear systems via LPV linear embeddings
Casavola, A., Famularo, D., & Franzè, G. (2003) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.818 · Cited by 65
Imsland, L., Findeisen, R., Bullinger, E., Allgöwer, F., & Foss, B. A. (2003) · Journal of Process Control · doi:10.1016/s0959-1524(03)00006-4 · Cited by 65
A business driven cloud optimization architecture
Litoiu, M., Woodside, M., Wong, J. W., Ng, J., & Iszlai, G. (2010) · ACM Symposium on Applied Computing · doi:10.1145/1774088.1774170 · Cited by 64
Benallouch, M., Schutz, G., Fiorelli, D., & Boutayeb, M. (2014) · Journal of Process Control · doi:10.1016/j.jprocont.2014.04.008 · Cited by 64
Integrated production scheduling and model predictive control of continuous processes
Bâldea, M., Du, J., Park, J., & Harjunkoski, I. (2015) · AIChE Journal · doi:10.1002/aic.14951 · Cited by 64
A hierarchical scheduling and control strategy for thermal energy storage systems
Touretzky, C. R., & Bâldea, M. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.09.049 · Cited by 64
Chen, W., O’Reilly, J., & Ballance, D. J. (2003) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.731 · Cited by 64
Ding, B. (2011) · Automatica · doi:10.1016/j.automatica.2011.08.038 · Cited by 64
Real-time reservoir management: A multiscale adaptive optimization and control approach
Saputelli, L. A., Nikolaou, M., & Economides, M. J. (2006) · Computational Geosciences · doi:10.1007/s10596-005-9011-5 · Cited by 64
Active mode observability of switching linear systems
Baglietto, M., Battistelli, G., & Scardovi, L. (2007) · Automatica · doi:10.1016/j.automatica.2007.01.006 · Cited by 64
A dynamic fuzzy model for a drum–boiler–turbine system
Habbi, H., Zelmat, M., & Bouamama, B. O. (2003) · Automatica · doi:10.1016/s0005-1098(03)00075-x · Cited by 64
Distributed synthesis and control of constrained linear systems
Conte, C., Voellmy, N. R., Zeilinger, M. N., Morari, M., & Jones, C. N. (2012) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2012.6314654 · Cited by 64
Multiplexed Predictive Control of a Large Commercial Turbofan Engine
Richter, H., Singaraju, A. V., & Litt, J. S. (2008) · Journal of Guidance Control and Dynamics · doi:10.2514/1.30591 · Cited by 64
Data-based hybrid modelling of the component placement process in pick-and-place machines
Juloski, A. L., Heemels, W. P. M. H., & Giancarlo Ferrari-trecate, A. (2004) · Control Engineering Practice · doi:10.1016/j.conengprac.2004.04.004 · Cited by 64
Stability analysis and controller synthesis for hybrid dynamical systems
Heemels, W. P. M. H., Schutter, B. D., Lunze, J., & Lazar, M. (2010) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2010.0187 · Cited by 61
Continuity and smoothness properties of nonlinear optimization-based feedback controllers
Morris, B., Powell, M. J., & Ames, A. D. (2015) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2015.7402101 · Cited by 64
Receding horizon tracking control of unicycle-type robots based on virtual structure
Sun, Z., & Xia, Y. (2016) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.3555 · Cited by 64
Jin, X. (2016) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.2734 · Cited by 64
Real-Time Control for Autonomous Racing Based on Viability Theory
Liniger, A., & Lygeros, J. (2017) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2017.2772903 · Cited by 64
Teng, L., Wang, Y., Cai, W., & Li, H. (2017) · Journal of Process Control · doi:10.1016/j.jprocont.2016.11.012 · Cited by 64
Optimal energy-water management in urban residential buildings through grey water recycling
Wanjiru, E. M., & Xia, X. (2017) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.05.009 · Cited by 64
Near-optimal control of nonlinear dynamical systems: A brief survey
Zhang, Y., Li, S., & Liao, L. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.01.003 · Cited by 64
Recent developments in the control of constrained hybrid systems
Morari, M., & Barić, M. (2006) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2006.05.041 · Cited by 64
Saak, J. (2009) · Qucosa (Saxon State and University Library Dresden) · Cited by 64
Trajectory tracking control of a VTOL unmanned aerial vehicle using offset-free tracking MPC
Manzoor, T., Xia, Y., Zhai, D., & Ma, D. (2020) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2020.03.003 · Cited by 63
Path tracking based on model predictive control with variable predictive horizon
Wang, H., Wang, Q., Chen, W., Zhao, L., & Tan, D. (2021) · Transactions of the Institute of Measurement and Control · doi:10.1177/01423312211003809 · Cited by 63
“…output y v at time k satisfies condition (3) and D d (cid:5) t t j ð Þ 2 ø 0 , the difference of Lyapunov function between two adjacent timeslots is Therefore, according to the stability theorem (Mayne et al., 2000), the closed-loop stability of the proposed model predictive controller is proved.”
BackgroundEvent-triggered robust MPC of nonlinear cyber-physical systems against DoS attacks
Sun, Q., Chen, J., & Shi, Y. (2021) · Science China Information Sciences · doi:10.1007/s11432-020-3289-1 · Cited by 63
“MPC can generate control law based on the optimal control and state sequences obtained by repeatedly solving constrained optimization problems with future system performance as ob-jective functions [12].”
MethodMulti-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction
He, S., Guo, S., Zhang, J., Liu, Z., Cui, Z., Zhang, Y., & Zheng, Y. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.127936 · Cited by 63
Application of explicit model predictive control to a hybrid battery-ultracapacitor power source
Hredzak, B., Agelidis, V. G., & Demetriades, G. D. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.11.148 · Cited by 63
UAVs Trajectory Planning by Distributed MPC under Radio Communication Path Loss Constraints
Grancharova, A., Grøtli, E. I., Ho, T. D., & Johansen, T. A. (2014) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0090-1 · Cited by 63
Desensitizing the Minimum-Fuel Powered Descent For Mars Pinpoint Landing
Shen, H., Seywald, H., & Powell, R. W. (2009) · Journal of Guidance Control and Dynamics · doi:10.2514/1.44649 · Cited by 63
Assessment of non-centralised model predictive control techniques for electrical power networks
Hermans, R. M., Jokić, A., Lazar, M., Alessio, A., van den Bosch, P. P. J., Hiskens, I. A., & Bemporad, A. (2012) · International Journal of Control · doi:10.1080/00207179.2012.679972 · Cited by 63
Performance Evaluation of Rendezvous Using Model Predictive Control
Richards, A. G., & How, J. P. (2003) · AIAA Guidance, Navigation, and Control Conference and Exhibit · doi:10.2514/6.2003-5507 · Cited by 63
LQ control for constrained continuous-time systems
Kojima, A., & Morari, M. (2004) · Automatica · doi:10.1016/j.automatica.2004.02.007 · Cited by 63
On the computation of linear model predictive control laws
Borrelli, F., Baotić, M., Pekař, J., & Stewart, G. E. (2010) · Automatica · doi:10.1016/j.automatica.2010.02.031 · Cited by 63
A Model Predictive Control approach for semi-active suspension control problem of a full car
Nguyen, M. Q., Canale, M., Sename, O., & Luc Dugard, A. (2016) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2016.7798353 · Cited by 63
Convex Control Systems and Convex Optimal Control Problems With Constraints
Azhmyakov, V., & Raisch, J. (2008) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.919848 · Cited by 63
Toward infinite-horizon optimality in nonlinear model predictive control
Hu, B., & Linnemann, A. (2002) · IEEE Transactions on Automatic Control · doi:10.1109/9.995049 · Cited by 63
STOCHASTIC MODEL PREDICTIVE CONTROL AND PORTFOLIO OPTIMIZATION
Herzog, F., Dondi, G., & Geering, H. P. (2007) · International Journal of Theoretical and Applied Finance · doi:10.1142/s0219024907004196 · Cited by 63
Using Predictions in Online Optimization
Chen, N., Comden, J., Liu, Z., Gandhi, A., & Wierman, A. (2016) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/2896377.2901464 · Cited by 62
Optimizing Model Predictive Control horizons using Genetic Algorithm for Motion Cueing Algorithm
Mohammadi, A., Asadi, H., Mohamed, S. M. K., Nelson, K. J., & Nahavandi, S. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.004 · Cited by 62
LMI-based model predictive control for switched nonlinear systems
Nodozi, I., & Rahmani, M. (2017) · Journal of Process Control · doi:10.1016/j.jprocont.2017.09.001 · Cited by 62
Sun, W., Li-ping, W., & Wu, Y. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.2969289 · Cited by 62
“Because of the practical needs, there are different techniques to tackle the constraint in the control design including reference governors [1], the set invariance notions [8], model predictive control [9], barrier Lyapunov function (BLF) method [10]–[17], and so on.”
MethodCassagnole, M., Ramos, M., Zalachori, I., Thirel, G., Garçon, R., Gailhard, J., & Ouillon, T. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-1033-2021 · Cited by 62
“…refers to the efficiency of the power plant, in MWh/m3.s−1, which is a constant equal to 1 MWh/m3.s−1 in this study; qh refers to the275 release in m3/s used for the production at hour h. Mayne et al. (2000) classify management constraints into two categories: hard constraints and soft constraints.”
MethodHan, Y., Wang, M., He, Z., Li, Z., Wang, H., & Liu, P. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103121 · Cited by 62
T-S Fuzzy Model Predictive Control for Vehicle Yaw Stability in Nonlinear Region
Wei, L., Wang, X., Li, L., Fan, Z., Dou, R., & Lin, J. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3091809 · Cited by 62
“Model predictive control (MPC) can handle the multi-objective optimization and control problems by predicting the response of the system in the near future, so it is widely implemented in YSC and autonomous vehicles [10], [11].”
BackgroundCooperative Optimal Control of the Following Operation of High-Speed Trains
Liu, H., Yang, L., & Yang, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3163971 · Cited by 62
Oh, J., & Bartos, M. (2023) · Water Research · doi:10.1016/j.watres.2023.119825 · Cited by 62
Li, D., Xi, Y., & Zheng, P. (2009) · International Journal of Control · doi:10.1080/00207170802530883 · Cited by 62
Exergy-based optimal control of a vapor compression system
Jain, N., & Alleyne, A. G. (2015) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.12.014 · Cited by 62
Liu, C., Negenborn, R. R., Chu, X., & Zheng, H. (2017) · Journal of Marine Science and Technology · doi:10.1007/s00773-017-0486-2 · Cited by 62
Robust Hybrid Nonlinear Control Systems for the Dynamics of a Quadcopter Drone
Santoso, F., Garratt, M., Anavatti, S. G., & Petersen, I. R. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2836922 · Cited by 62
Data-Based Receding Horizon Control of Linear Network Systems
Allibhoy, A., & Cortes, J. (2020) · IEEE Control Systems Letters · doi:10.1109/lcsys.2020.3021050 · Cited by 62
Improving ReduceTask data locality for sequential MapReduce jobs
Tan, J. R., Meng, S., Meng, X., & Zhang, L. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6566959 · Cited by 61
Synthesis of ILC–MPC Controller With Data-Driven Approach for Constrained Batch Processes
Li, D., He, S., Xi, Y., Liu, T., Gao, F., Wang, Y., & Lu, J. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2910034 · Cited by 61
Online learning based risk-averse stochastic MPC of constrained linear uncertain systems
Ning, C., & You, F. (2020) · Automatica · doi:10.1016/j.automatica.2020.109402 · Cited by 61
Reinforcement Learning of the Prediction Horizon in Model Predictive Control
Bøhn, E., Gros, S., Moe, S., & Johansen, T. A. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.563 · Cited by 61
Xu, Z., Qi, G., Liu, Q., & Yao, J. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108560 · Cited by 61
Liu, C., Hu, Q., Wang, X., & Yin, J. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.111278 · Cited by 61
Soft computing in modelbased predictive control footnotemark
Tatjewski, P., & Ławryńczuk, M. (2006) · International Journal of Applied Mathematics and Computer Science · Cited by 61
Nonlinear model predictive control from data: a set membership approach
Canale, M., Fagiano, L., & Signorile, M. C. (2012) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.2878 · Cited by 61
Model Predictive Control of Autonomous Vehicles
Zanon, M., Frasch, J. V., Vukov, M., Säger, S., & Diehl, M. (2014) · Lecture notes in control and information sciences · doi:10.1007/978-3-319-05371-4_3 · Cited by 61
MPC on a chip—Recent advances on the application of multi-parametric model-based control
Dua, P., Kouramas, K., Dua, V., & Pistikopoulos, E. N. (2007) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2007.03.008 · Cited by 61
Output feedback design for saturated linear plants using deadzone loops
Dai, D., Hu, T., Teel, A. R., & Zaccarian, L. (2009) · Automatica · doi:10.1016/j.automatica.2009.09.022 · Cited by 61
Zhang, T., Feng, G., & Zeng, X. (2009) · Automatica · doi:10.1016/j.automatica.2008.11.016 · Cited by 61
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