References
Xi Y G, Li D W, Lin S. Model predictive control — status and challenges. Acta Automatica Sin, 2013, 39: 222–236
Mayne D Q, Rawlings J B, Rao C V, et al. Constrained model predictive control: stability and optimality. Automatica, 2000, 36: 789–814
Li D, Xi Y, Gao F. Synthesis of dynamic output feedback RMPC with saturated inputs. Automatica, 2013, 49: 949–954
Chen W H, Ballance D J, Gawthrop P J. Optimal control of nonlinear systems: a predictive control approach. Automatica, 2003, 39: 633–641
Albin T. Benefits of model predictive control for gasoline airpath control. Sci China Inf Sci, 2018, 61: 070204
Jiang Y, Jiang Z P. Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics. Automatica, 2012, 48: 2699–2704
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61973214, 61590924, 61963030), Natural Science Foundation of Shanghai (Grant No. 19ZR1476200), and National Key Basic Research Special Foundation of China (Grant No. 2014CB249200). The authors would like to thank Prof. Zhong-Ping JIANG and his CAN Lab at Tandon School of Engineering, New York University, Brooklyn, NY, USA, for many inspirations and the help of this work.
Author information
Authors and Affiliations
Corresponding author
Supplementary File
Rights and permissions
About this article
Cite this article
Zhou, Y., Li, D., Xi, Y. et al. Synthesis of model predictive control based on data-driven learning. Sci. China Inf. Sci. 63, 189204 (2020). https://doi.org/10.1007/s11432-018-9645-3
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-018-9645-3