On 35 Approaches for Distributed MPC Made Easy
In this chapter the motivation for developing a comprehensive overview of distributed MPC techniques such as presented in this book is discussed. Understanding the wide range of techniques available becomes easier when a common structure and notation is a
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On 35 Approaches for Distributed MPC Made Easy R. R. Negenborn and J. M. Maestre
Abstract In this chapter the motivation for developing a comprehensive overview of distributed MPC techniques such as presented in this book is discussed. Understanding the wide range of techniques available becomes easier when a common structure and notation is adopted. Therefore, a list of questions is proposed that can be used to obtain a structured way in which such techniques can be described, and a preferred notation is suggested. This chapter concludes with an extensive categorization of the techniques described in this book, and compact representations of the properties of each individual technique. As such, this chapter serves as a starting point for further developing understanding of the various particularities of the different techniques.
1.1 Introduction 1.1.1 From Centralized to Distributed Control The evolution of computer science and information technology has made possible the application of control techniques to systems that were beyond the possibilities of control theory just a decade ago. The size of the problems faced today by control engineers has grown enormously as the limitations imposed by the communication and computational capabilities decrease. In this sense, there are strong incentives to be ambitious: society heavily depends on infrastructure systems, such as road-traffic networks, water networks, electricity networks, intermodal transport networks, etc. R. R. Negenborn (B) Department of Marine and Transport Technology, Delft University of Technology, Delft, The Netherlands e-mail: [email protected] J. M. Maestre Departamento de Sistemas y Automática, Universidad de Sevilla, Sevilla, Spain 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_1, © Springer Science+Business Media Dordrecht 2014
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R. R. Negenborn and J. M. Maestre
These are examples of large-scale, networked systems, that everybody makes use of on a daily basis, and any performance improvement would have a great direct impact on the society. However, traditional centralized control approaches cannot be used with these kind of systems due to, e.g., centralized computational issues or issues with centralized modeling, data collection and actuation. It is at this point where distributed controllers come into play. The idea behind distributed control approaches is simple: the centralized problem is divided in several different parts whose control is assigned to a certain number of local controllers or agents. Therefore, each agent does not have a global vision of the problem. Depending on the degree of interaction that exists between the local subsystems, the agents may need to communicate so that they can coordinate themselves. Distributed approaches have important advantages that justify their use. The first advantage is that in general these schemes are e
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