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Stochastic Model Predictive Control: An Overview and Perspectives for Future Research
Open accessAbstract
Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach. This article gives an overview of the main developments in the area of stochastic model predictive control (SMPC) in the past decade and provides the reader with an impression of the different SMPC algorithms and the key theoretical challenges in stochastic predictive control without undue mathematical complexity. The general formulation of a stochastic OCP is first presented, followed by an overview of SMPC approaches for linear and nonlinear systems. Suggestions of some avenues for future research in this rapidly evolving field concludes the article.
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Cites
Constrained model predictive control: Stability and optimality
Mayne, D. Q., Rawlings, J. B., Rao, C. V., & Scokaert, P. O. M. (2000) · Automatica · doi:10.1016/s0005-1098(99)00214-9 · Cited by 8,736
Cited by
All you need to know about model predictive control for buildings
Drgoňa, J., Arroyo, J., Cupeiro Figueroa, I., Blum, D., Arendt, K., Kim, D., Ollé, E. P., Oravec, J., Wetter, M., Vrabie, D. L., et al. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.09.001 · Cited by 1,020
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Hewing, L., Wabersich, K. P., Menner, M., & Zeilinger, M. N. (2020) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-090419-075625 · Cited by 835
Reference and command governors for systems with constraints: A survey on theory and applications
Garone, E., Di Cairano, S., & Kolmanovsky, I. (2017) · Automatica · doi:10.1016/j.automatica.2016.08.013 · Cited by 466
Metadata
- Venue
- IEEE Control Systems
- Published
- 2016-12
- Volume
- 36
- Issue
- 6
- Pages
- 30-44
- ISSN
- 1066-033X (ISSN-L)
- 1941-000X
- OpenAlex
- W2557055507
- Cited by
- 923
- References
- 1
- FWCI
- 50.78
- Type
- journal article
- Language
- en
- Open access
- green
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