Robust learning-based MPC for nonlinear constrained systems
Open accessAbstract
Sherpa/Romeo: Versión aceptada en repositorios institucionales tras 24 meses de embargo https://v2.sherpa.ac.uk/id/publication/4278
Integrity signals
Registry
Cites
Statistics
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?
A data-driven MPC uses a nonparametric machine learning technique to estimate a prediction model, but it further increases the computational complexity of MPC.
“A data-driven MPC was developed in [24], where a nonparametric machine learning technique is used to estimate a prediction model. Unfortunately, learning and data-driven techniques further increase the computational complexity of MPC.”
From Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator (2022) · cites this paper as [24]
Cites
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 5,351 · see citing works
Söderström, T., & Stoica, P. (1988) · Cited by 5,116 · see citing works
Input-output parametric models for non-linear systems Part I: deterministic non-linear systems
LEONTARITIS, I. J., & Billings, S. A. (1985) · International Journal of Control · doi:10.1080/0020718508961129 · Cited by 1,145 · see citing works
Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
LEONTARITIS, I. J., & Billings, S. A. (1985) · International Journal of Control · doi:10.1080/0020718508961130 · Cited by 1,067 · see citing works
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Hewing, L., Wabersich, K. P., Menner, M., & Zeilinger, M. N. (2019) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-090419-075625 · Cited by 849 · see citing works
Provably safe and robust learning-based model predictive control
Aswani, A. J., González, H., Sastry, S., & Tomlin, C. J. (2013) · Automatica · doi:10.1016/j.automatica.2013.02.003 · Cited by 540 · see citing works
A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems
Fisac, J. F., Akametalu, A. K., Zeilinger, M. N., Kaynama, S., Gillula, J., & Tomlin, C. J. (2018) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2018.2876389 · Cited by 460 · see citing works
A stabilizing model-based predictive control algorithm for nonlinear systems
Magni, L., Nicolao, G. D., Magnani, L., & Scattolini, R. (2001) · Automatica · doi:10.1016/s0005-1098(01)00083-8 · Cited by 356 · see citing works
Smarra, F., Jain, A., de Rubeis, T., Ambrosini, D., D’Innocenzo, A., & Mangharam, R. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.126 · Cited by 340 · see citing works
Omidvar, O., & Elliott, D. (1997) · Elsevier eBooks · doi:10.1016/b978-0-12-526430-3.x5000-4 · Cited by 275 · see citing works
Set Membership identification of nonlinear systems
Milanese, M., & Novara, C. (2004) · Automatica · doi:10.1016/j.automatica.2004.02.002 · Cited by 267 · see citing works
Input-to-State Stability: A Unifying Framework for Robust Model Predictive Control
Limón, D., Álamo, T., Raimondo, D. M., de la Peña, D. M., Bravo, J. M., Ferramosca, A., & Camacho, E. F. (2009) · Lecture notes in control and information sciences · doi:10.1007/978-3-642-01094-1_1 · Cited by 261 · see citing works
Signal Analysis and Prediction
Kingsbury, N. G., Rayner, P., Uhlir, J., et al. (1998) · Applied and numerical harmonic analysis · doi:10.1007/978-1-4612-1768-8 · Cited by 252 · see citing works
On the stability of constrained MPC without terminal constraint
Limón, D., Álamo, T., Salas, F., & Camacho, E. F. (2006) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2006.875014 · Cited by 228 · see citing works
Probabilistic and Randomized Methods for Design under Uncertainty
Calafiore, G. C., & Dabbene, F. (2006) · Springer eBooks · doi:10.1007/b138725 · Cited by 212 · see citing works
Direct Data-Driven Control of Constrained Systems
Piga, D., Formentin, S., & Bemporad, A. (2017) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2017.2702118 · Cited by 115 · see citing works
On the computation of convex robust control invariant sets for nonlinear systems
Fiacchini, M., Álamo, T., & Camacho, E. F. (2010) · Automatica · doi:10.1016/j.automatica.2010.05.007 · Cited by 94 · see citing works
Interpolation of Lipschitz functions
Beliakov, G. (2005) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2005.08.011 · Cited by 78 · see citing works
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 · see citing works
Conservative decision-making and inference in uncertain dynamical systems
Calliess, J.-P. (2014) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-xrp0d4yqy · Cited by 54 · see citing works
Self-citationCalliess, J.-P., Roberts, S. J., Rasmussen, C. E., et al. (2020) · Automatica · doi:10.1016/j.automatica.2020.109216 · Cited by 44 · see citing works
Self-citationStability of Gaussian Process Learning Based Output Feedback Model Predictive Control
Maiworm, M., Limón, D., Manzano, J. M., et al. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.11.047 · Cited by 43 · see citing works
Self-citationOutput feedback MPC based on smoothed projected kinky inference
Manzano, J. M., Limón, D., de la Peña, D. M., et al. (2019) · IET Control Theory and Applications · doi:10.1049/iet-cta.2018.5522 · Cited by 38 · see citing works
Self-citationProbabilistic and Randomized Methods for Design Under Uncertainty
Sandry, T. D. (2007) · Technometrics · doi:10.1198/tech.2007.s485 · Cited by 37 · see citing works
Optimal estimation of univariate black-box Lipschitz functions with upper and lower error bounds
Zabinsky, Z. B., Smith, R. L., & Kristinsdottir, B. P. (2003) · Computers & Operations Research · doi:10.1016/s0305-0548(02)00082-5 · Cited by 33 · see citing works
Optimal method of constructing best uniform approximations for functions of a certain class
Sukharev, A. G. (1978) · USSR Computational Mathematics and Mathematical Physics · doi:10.1016/0041-5553(78)90035-6 · Cited by 24 · see citing works
Data-driven modeling, control and tools for cyber-physical energy systemsPreprint
Behl, M., Jain, A., & Mangharam, R. (2016) · arXiv (Cornell University) · doi:10.5555/2984464.2984499 · Cited by 23 · see citing works
Identification of Nonlinear Dynamical Systems Using Neural Networks
Levin, A. U., & Narendra, K. S. (1997) · Elsevier eBooks · doi:10.1016/b978-012526430-3/50007-6 · Cited by 18 · see citing works
Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approachPreprint
Piga, D., Formentin, S., & Bemporad, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04447 · Cited by 15 · see citing works
Robust Data-Based Model Predictive Control for Nonlinear Constrained Systems
Manzano, J. M., Limón, D., de la Peñ, D. M., et al. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.11.039 · Cited by 15 · see citing works
Self-citationOffset free data driven control: application to a process control trainer
Salvador, J. R., Ramı́rez, D. R., Álamo, T., et al. (2019) · IET Control Theory and Applications · doi:10.1049/iet-cta.2019.0376 · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Incremental Model Predictive Control Exploiting Time-Delay Estimation for a Robot Manipulator
Wang, Y., Leibold, M., Lee, J., Ye, W., Xie, J., & Buss, M. (2022) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2022.3142629 · Cited by 52 · see citing works
- Authors
- Manzano, J. M., Limon, D., Muñoz de la Peña, D., & Calliess, J.-P.
- Affiliations
- Universidad de Sevilla (ES)
- University of Oxford (GB)
- Corresponding author
- Manzano, José María
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 117
- Pages
- 108948
- ISSN
- 0005-1098 (ISSN-L)
- OpenAlex
- W3014803699
- Cited by
- 87
- References
- 32
- Citations by year
- 2026: 5 · 2025: 22 · 2024: 12 · 2023: 13 · 2022: 19 · 2021: 12 · 2020: 4
- FWCI
- 5.93
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Automatica (publishedVersion)
- Deposito de Investigacion Universidad de Sevilla (University of Seville) (submittedVersion, cc-by-nc-nd)
- Funders
- Universidad de Sevilla
- Ministerio de Economía y Competitividad
- 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
- robust model predictive control, learning control, data-driven 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