Distributed MPC for Consensus and Synchronization
In this chapter, we describe a distributed MPC algorithm for cooperative control of a network of systems which are coupled by constraints and pursue a common, cooperative control objective. The proposed DMPC algorithm cannot only be used for classical con
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Distributed MPC for Consensus and Synchronization M. A. Müller and F. Allgöwer
Abstract In this chapter, we describe a distributed MPC algorithm for cooperative control of a network of systems which are coupled by constraints and pursue a common, cooperative control objective. The proposed DMPC algorithm cannot only be used for classical control objectives such as set point stabilization, but also for more general cooperative control tasks such as consensus and synchronization problems. Possible application fields include teams of mobile robots, formation flight of aircrafts, as well as satellite control.
5.1 Introduction Most of the distributed MPC algorithms described in this book consider the problem of solving a centralized problem for an overall large-scale system by distributing it into several coupled subproblems. These subproblems are then assigned to local controllers or agents, which together compute a (approximate) solution to the original centralized problem in a distributed or decentralized fashion. In this chapter, we look at distributed MPC from a different point of view. Namely, we consider the setup of a team of physically (i.e., dynamically) decoupled systems which pursue a common, cooperative control task and which additionally may have to satisfy certain coupling constraints. This setup includes, for example, formation flight of a team of aircrafts subject to collision avoidance constraints, or remote sensing of a team of satellites with connectivity maintenance constraints. In such a setting, more general cooperative control objectives are of great importance, compared to classical ones M. A. Müller (B) · F. Allgöwer University of Stuttgart, Institute for Systems Theory and Automatic Control, Stuttgart, Germany e-mail: [email protected] F. Allgöwer e-mail: [email protected]
J. M. Maestre and R. R. Negenborn (eds.), Distributed Model Predictive Control Made Easy, Intelligent Systems, Control and Automation: Science and Engineering 69, DOI: 10.1007/978-94-007-7006-5_5, © Springer Science+Business Media Dordrecht 2014
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such as the stabilization of an a priori known set point. In particular, consensus and synchronization problems are of interest (see, e.g., [3, 10, 11] and the references therein), where the systems have to agree on a common trajectory online, in contrast to following an a priori specified reference trajectory. In this chapter, which is based on the results presented in [8, 9], we describe a distributed MPC algorithm which can be used for a variety of cooperative control tasks for dynamically independent systems including the above mentioned. The proposed algorithm is non-iterative and the systems optimize their performance criteria in a specific order, similar to [13], where such an idea was used for the robust stabilization of a set point for systems only coupled via constraints. The advantage of such an approach is that less communication between the systems is needed in comparison to iterative scheme
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