The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Robust model predictive control of constrained linear systems with bounded disturbances
Abstract
This paper provides a novel solution to the problem of robust model predictive control of constrained, linear, discrete-time systems in the presence of bounded disturbances. The optimal control problem that is solved online includes, uniquely, the initial state of the model employed in the problem as a decision variable. The associated value function is zero in a disturbance invariant set that serves as the 'origin' when bounded disturbances are present, and permits a strong stability result, namely robust exponential stability of the disturbance invariant set for the controlled system with bounded disturbances, to be obtained. The resultant online algorithm is a quadratic program of similar complexity to that required in conventional model predictive control.
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Citations
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
Self-citation
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Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Brunke, L., Greeff, M., Hall, A. W., Yuan, Z., Zhou, S., Panerati, J., & Schoellig, A. P. (2022) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-042920-020211 · Cited by 769
Optimization over state feedback policies for robust control with constraints
Goulart, P. J., Kerrigan, E. C., & Maciejowski, J. M. (2006) · Automatica · doi:10.1016/j.automatica.2005.08.023 · Cited by 580
Tube‐based robust nonlinear model predictive control
Mayne, D. Q., Kerrigan, E. C., van Wyk, E. J., & Falugi, P. (2011) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.1758 · Cited by 462
Self-citationLearning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487175 · Cited by 452
“Model predictive control (MPC) is an effective and reliable method for controlling robotic systems, particularly autonomous aerial vehicles such as quadcopters, because of its robustness to moderate model errors [26], ability to use high-level objectives [41], and relative simplicity.”
MethodFarina, M., & Scattolini, R. (2012) · Automatica · doi:10.1016/j.automatica.2012.03.020 · Cited by 278
Model Predictive Control approach for guidance of spacecraft rendezvous and proximity maneuvering
Di Cairano, S., Park, H., & Kolmanovsky, I. (2012) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.2827 · Cited by 273
Disturbance Compensating Model Predictive Control With Application to Ship Heading Control
Li, Z., & Sun, J. (2011) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2011.2106212 · Cited by 168
A Distributionally Robust Optimization Based Method for Stochastic Model Predictive Control
Li, B., Tan, Y., Wu, A.-G., & Duan, G.-R. (2022) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2021.3124750 · Cited by 156
Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview
Tsukamoto, H., Chung, S.-J., & Slotine, J.-J. E. (2021) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2021.10.001 · Cited by 145
Event-Triggered Robust Distributed MPC for Multi-Agent Systems with A Two-Step Event Verification
Deng, L., Shu, Z., & Chen, T. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.11.322 · Cited by 2
A Robust Model Predictive Control Method for Networked Control Systems
Beger, S., & Hirche, S. (2024) · 2024 IEEE 63rd Conference on Decision and Control (CDC) · doi:10.1109/cdc56724.2024.10886340 · Cited by 2
Metadata
- Authors
- Mayne, D. Q., Seron, M. M., & Raković, S. V.
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 2005-02
- Volume
- 41
- Issue
- 2
- Pages
- 219-224
- ISSN
- 0005-1098 (ISSN-L)
- OpenAlex
- W1981723834
- Cited by
- 1,672
- References
- 1
- FWCI
- 31.40
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
