Abstract
This chapter presents a hierarchical distributed model predictive control algorithm. Two levels in the problem optimization are presented. At the lower level, a distributed model predictive controller optimizes the operation of the plant manipulating the control variables in order to follow the set-points. The higher level implements a risk management strategy based on the execution of mitigation actions if risk occurrences are expected. In this way it is possible to take into account additional relevant information so that better results are achieved in the optimization of the system.
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Acknowledgments
Financial support from the HYCON2 EU-project from the ICT-FP7 and MEC-Spain, DPI2008-05818, and F.P.I. grants is gratefully acknowledged.
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© 2014 Springer Science+Business Media Dordrecht
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Zafra-Cabeza, A., Maestre, J.M. (2014). A Hierarchical Distributed MPC Approach: A Practical Implementation. In: Maestre, J., Negenborn, R. (eds) Distributed Model Predictive Control Made Easy. Intelligent Systems, Control and Automation: Science and Engineering, vol 69. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7006-5_28
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DOI: https://doi.org/10.1007/978-94-007-7006-5_28
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