The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Distributed MPC Strategies With Application to Power System Automatic Generation Control
Abstract
A distributed model predictive control (MPC) framework, suitable for controlling large-scale networked systems such as power systems, is presented. The overall system is decomposed into subsystems, each with its own MPC controller. These subsystem-based MPCs work iteratively and cooperatively towards satisfying systemwide control objectives. If available computational time allows convergence, the proposed distributed MPC framework achieves performance equivalent to centralized MPC. Furthermore, the distributed MPC algorithm is feasible and closed-loop stable under intermediate termination. Automatic generation control (AGC) provides a practical example for illustrating the efficacy of the proposed distributed MPC framework.
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Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Cited by
Farina, M., & Scattolini, R. (2012) · Automatica · doi:10.1016/j.automatica.2012.03.020 · Cited by 278
Attack detection and identification for automatic generation control systems
Ameli, A., Hooshyar, A., El-Saadany, E. F., & Youssef, A. M. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2810161 · Cited by 222
Metadata
- Authors
- Venkat, A. N., Hiskens, I. A., Rawlings, J. B., & Wright, S. J.
- Venue
- IEEE Transactions on Control Systems Technology
- Published
- 2008-11
- Volume
- 16
- Issue
- 6
- Pages
- 1192-1206
- ISSN
- 1063-6536 (ISSN-L)
- 1558-0865
- OpenAlex
- W2131869695
- Cited by
- 811
- References
- 2
- FWCI
- 60.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- Venkat, A. N., Hiskens, I. A., Rawlings, J. B., & Wright, S. J. (2008). Distributed MPC Strategies With Application to Power System Automatic Generation Control. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2008.919414
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