Abstract
In this chapter we propose a distributed model predictive control scheme based on agent negotiation. In particular, we consider the control of several subsystems coupled through the inputs by a set of independent agents that are able to communicate and we assume that each agent has access only to the model and the state of one of the subsystems. This implies that in order to take a cooperative decision, i.e. for the whole system, the agents must negotiate. At each sampling time, following a given protocol, agents make proposals to improve an initial feasible solution on behalf of their local cost function, state and model. These proposals are accepted if the global cost improves the cost corresponding to the current solution. In addition, we study the stability properties of the proposed distributed controller and provide precise conditions that guarantee that the closed-loop system is practically stable along with an optimization based controller and invariant design procedure.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The symbol “\(*\)” stands for the symmetric part of a matrix.
References
J. M. Maestre, Distributed Model Predictive Control Based on Game Theory. PhD thesis, Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Nov 2010
J.M. Maestre, M.D. Doan, D. Muñoz de la Peña, P.J. van Overloop, T. Keviczky, M. Ridao, B. De Schutter, Benchmarking the operation of a hydro power network through the application of agent-based model predictive controllers, in Proceedings of the 10th International Conference on Hydroinformatics, July 2012
J.M. Maestre, D. Muñoz de la Peña, E.F. Camacho, Distributed MPC: a supply chain case study, in Proceedings of the joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, 2009
J.M. Maestre, D. Muñoz de la Peña, E.F. Camacho, Distributed model predictive control based on a cooperative game. Optimal Control Appl. Methods 32(2), 153–176 (2011)
J.M. Maestre, D. Muñoz de la Peña, E.F. Camacho, T. Álamo, Distributed model predictive control based on agent negotiation. J Process Control 21(5), 685–697 (June 2011)
D.Q. Mayne, J.B. Rawlings, C.V. Rao, P.O.M. Scokaert, Constrained model predictive control: Stability and optimality. Automatica 36, 789–814 (2000)
S.V. Rakovic, E. De Santis, P. Caravani. Invariant equilibria of polytopic games via optimized robust control invariance. in Proceedings of the 44th IEEE Conference on Decision and Control and the European Control Conference, pp. 7686–7691, Seville, Spain, Dec 2005
P.O.M. Scokaert, D.Q. Mayne, J.B. Rawlings, Suboptimal model predictive control (feasibility implies stability). IEEE Trans. Autom. Control 44, 648–654 (1999)
A. Zafra-Cabeza, J.M. Maestre, M.A. Ridao, E.F. Camacho, L. Sánchez, A hierarchical distributed model predictive control approach in irrigation canals: A risk mitigation perspective. J Process Control 21(5), 787–799 (2011)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Maestre, J.M., Muñoz de la Peña, D., Camacho, E.F. (2014). Distributed MPC Based on Agent Negotiation. 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_29
Download citation
DOI: https://doi.org/10.1007/978-94-007-7006-5_29
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7005-8
Online ISBN: 978-94-007-7006-5
eBook Packages: EngineeringEngineering (R0)