by R.R. Negenborn, J.M. Maestre 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 … Continue reading
by J.M. Maestre, D. Muñoz de la Peña, E.F. Camacho 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 … Continue reading
by J.L. Nabais, R.R. Negenborn, R.B. Carmona-Benítez, L.F. Mendonça, M.A. Botto Transportation networks are large scale complex systems spatially distributed whose objective is to deliver commodities at the agreed time and at the agreed location. These networks appear in different … Continue reading
by R.R. Negenborn In this chapter we described two distributed MPC schemes for control of interconnected time-invariant discrete-time linear systems: a scheme with serial iterations, and a scheme with parallel iterations. Under the given assumptions, the schemes converge to a … Continue reading
by A. Ferramosca In this chapter, a cooperative distributed MPC is presented. The main features of this control strategy are: constraints satisfaction; cooperation between agents to achieve an agreement; closed-loop stability that is always ensured, even in the case of … Continue reading
by A. Zafra-Cabeza and, J.M. Maestre 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 … Continue reading
by M.D. Doan, T. Keviczky, and B. De Schutter In this chapter we describe an iterative two-layer hierarchical approach to MPC of large-scale linear systems subject to coupled linear constraints. The algorithm uses constraint tightening and applies a primal-dual iterative … Continue reading
by J.M. Maestre, F.J. Muros, F. Fele, D. Munoz de la Pena, and E. F. Camacho In this chapter we present a distributed scheme based on a team game for the particular case in which the system is controlled by … Continue reading
by R. Marti, D. Sarabia, and C. de Prada This chapter presents a distributed coordinated control algorithm based on a hierarchical scheme for systems consisting of nonlinear subsystems coupled by input constraints: the botton layer is composed of several non-linear … Continue reading
by P. Giselsson, A. Rantzer We consider distributed model predictive control (DMPC) where a sparse centralized optimization problem without a terminal cost or a terminal constraint set is solved in distributed fashion. Distribution of the optimization algorithm is enabled by … Continue reading
by I. Necoara In this chapter we propose a dual decomposition method based on inexact dual gradient information and constraint tightening for solving distributed model predictive control (MPC) problems for network systems with state-input constraints. The coupling constraints are tightened … Continue reading
by Isabel Jurado, Daniel E. Quevedo, Karl H. Johansson and Anders Ahlen This work studies cooperative MPC for Networked Control Systems with multiple wireless nodes. Communication between nodes is affected by random packet dropouts. An algorithm is presented to decide … Continue reading
by Bernardo Morcego, Valeria Javalera, Vicenc¸ Puig and Raffaele Vito. This chapter describes a methodology to deal with the interaction (negotiation) between MPC controllers in a distributed MPC architecture. This approach combines ideas from Distributed Artificial Intelligence (DAI) and Reinforcement … Continue reading
by Benjamin Biegel, Jakob Stoustrup, and Palle Andersen. This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a localmodel predictive controller while a centralized … Continue reading
by Felipe Valencia, José David López, Julián Alberto Patiño, Jairo José Espinosa. Despite of the efforts dedicated to design methods for distributed model predictive control (DMPC), the cooperation among subsystems still remains as an open research problem. In order to … Continue reading
by Ye Hu and Nael H. El-Farra. This work presents a framework for quasi-decentralized model predictive control (MPC) design with an adaptive communication strategy. In this framework, each unit of the networked process system is controlled by a local control … Continue reading
by Farhad Farokhi, Iman Shames, and Karl H. Johansson. A conventional way to handle model predictive control (MPC) problems distributedly is to solve them via dual decomposition and gradient ascent. However, at each time-step, it might not be feasible to … Continue reading
by Gabriele Pannocchia, Stephen J. Wright, and James B. Rawlings. We address the problem of efficient implementations of distributed Model Predictive Control (MPC) systems for large-scale plants. We explore two possibilities of using suboptimal solvers for the quadratic program associated … Continue reading
by Jinfeng Liu, David Muñoz de la Peña and Panagiotis D. Christofides. In this chapter, we focus on two distributed MPC (DMPC) schemes for the control of large-scale nonlinear systems in which severaldistinct sets of manipulated inputs are used to … Continue reading
by Francesco Tedesco, Davide Martino Raimondo, Alessandro Casavola. This chapter deals with distributed coordination problems which include the fulfillment of non-convex constraints. A Distributed Command Governor (D-CG) strategy is here proposed to coordinate a set of dynamically decoupled subsystems. The … Continue reading
by Antonio Ferramosca, Daniel Limon and Alejandro H. González. In this chapter, a distributed MPC strategy suitable for changing setpoints is described. Based on a cooperative distributed control structure, an extended-cost MPC formulation is proposed, which integrates the problem of … Continue reading
by P. A. Trodden and A. G. Richards. This chapter presents a robust form of distributed model predictive control for multiple, dynamically decoupled subsystems subject to bounded, persistent disturbances. Control agents make decisions locally and exchange plans; satisfaction of coupling … Continue reading
by Attila Kozma, Carlo Savorgnan and Moritz Diehl. The distributed multiple shooting method is tailored for large scale optimal control problems with decoupled structure. It can be used as a fast and distributed solver for model predictive control subproblems. The … Continue reading
by João M. Lemos and José M. Igreja. This chapter describes D-SIORHC, a distributed MPC algorithm with stability constraints based on a game approach. This controller is designed for chained linear systems, in which each local subsystem interacts only with … Continue reading
by Mohamed Yacine Lamoudi, Mazen Alamir and Patrick Béguery. In this chapter, a hierarchical model predictive control framework is presented for a network of subsystems that are submitted to general resource sharing constraints. The method is based on a primal … Continue reading
by Alessandro Casavola, Emanuele Garone and Francesco Tedesco. The term Command Governor (CG) refers to a particular class of Model Predictive Control (MPC) strategies designed to manage the reference of a pre-compensated system in such a way that set-membership constraints … Continue reading
Samira Roshany-Yamchi, Rudy R. Negenborn and Avi A. Cornelio In this chapter, a new Nash-based distributed MPC method is proposed to control large-scale multi-rate systems with linear dynamics that are coupled via inputs. These systems are multi-rate systems in the … Continue reading
C. Ocampo-Martinez, V. Puig, J.M. Grosso and S. Montes-de-Oca In this chapter, a multi-layer decentralized model predictive control (MLDMPC) approach is proposed and designed for its application to large-scale networked systems (LSNS). This approach is based on the periodic nature … Continue reading
Ionela Prodan, Florin Stoican, Sorin Olaru, Cristina Stoica, Silviu-Iulian Niculescu This chapter proposes a distributed approach for the resolution of a multi-agent problem under collision and obstacle avoidance conditions. Using hyperplane arrangements and mixed integer programming, we provide an efficient … Continue reading
Daniel Axehill and Anders Hansson In this chapter parallel implementations of hybrid MPC will be discussed. Different methods for achieving parallelism at different levels of the algorithms will be surveyed. It will be seen that there are many possible ways … Continue reading
Ralph Hermans, Mircea Lazar and Andrej Jokic We provide an almost decentralized solution to the problem of stabilizing a network of discrete-time nonlinear systems with coupled dynamics that are subject to local state/input constraints. By “almost decentralized” we mean that … Continue reading
Alexandra Grancharova and Tor Arne Johansen A suboptimal approach to distributed Nonlinear Model Predictive Control (NMPC) for systems consisting of nonlinear subsystems with nonlinearly coupled dynamics subject to both state and input constraints is proposed. The approach applies a dynamic … Continue reading
Romain Bourdais, Jean Buisson, Didier Dumur, Hervé Guéguen, Daniel Moroscan In this chapter, we propose a distributed model predictive control scheme based on the Dantzig-Wolfe decomposition to control a collection of linear dynamical systems coupled by linear global constraints. The … Continue reading
Matthias A. Müller and Frank Allgöwer 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 … Continue reading
Eduardo Camponogara This chapter presents existing models and distributed optimization algorithms for model predictive control (MPC) of linear dynamic networks (LDNs). The models consist of networks of subsystems with deterministic and uncertain dynamics subject to local and coupling constraints on … Continue reading
Giulio Betti, Marcello Farina, Riccardo Scattolini The Distributed Predictive Control (DPC) algorithm presented in this chapter has been designed for control of an overall system made by linear discrete time dynamically interconnected subsystems. The underlying rationale is that each subsystem … Continue reading