Cooperative distributed MPC of linear systems with coupled constraints

This paper develops a cooperative, distributed form of MPC for linear systems subject to persistent, bounded disturbances. The distributed control agents make decisions locally and communicate plans with each other. Cooperation is promoted by consideration of a greater portion of the system-wide objective by each local agent; specifically, a local agent designs hypothetical plans for other agents, sacrificing local performance for the benefit of system-wide performance. These hypothetical plans are never communicated and no negotiation takes place. The method guarantees robust feasibility by permitting only one agent to optimize per time step, while 'freezing' the plans of others, and sufficient conditions are given for robust stability. These properties hold for all structures of cooperation between agents. Thus, a key feature is that coupled constraint satisfaction is compatible with inter-agent cooperation. © 2012 Elsevier Ltd. All rights reserved

Cooperative distributed MPC for tracking

This paper proposes a cooperative distributed linear model predictive control (MPC) strategy for tracking changing setpoints, applicable to any finite number of subsystems. The proposed controller is able to drive the whole system to any admissible setpoint in an admissible way, ensuring feasibility under any change of setpoint. It also provides a larger domain of attraction than standard distributed MPC for regulation, due to the particular terminal constraint. Moreover, the controller ensures convergence to the centralized optimum, even in the case of coupled constraints. This is possible thanks to the warm start used to initialize the optimization Algorithm, and to the design of the cost function, which integrates a Steady-State Target Optimizer (SSTO). The controller is applied to a real four-tank plant

Distributed Model Predictive Control Using a Chain of Tubes

A new distributed MPC algorithm for the regulation of dynamically coupled subsystems is presented in this paper. The current control action is computed via two robust controllers working in a nested fashion. The inner controller builds a nominal reference trajectory from a decentralized perspective. The outer controller uses this information to take into account the effects of the coupling and generate a distributed control action. The tube-based approach to robustness is employed. A supplementary constraint is included in the outer optimization problem to provide recursive feasibility of the overall controllerComment: Accepted for presentation at the UKACC CONTROL 2016 conference (Belfast, UK

Distributed Model Predictive Control with Asymmetric Adaptive Terminal Sets for the Regulation of Large-scale Systems

In this paper, a novel distributed model predictive control (MPC) scheme with asymmetric adaptive terminal sets is developed for the regulation of large-scale systems with a distributed structure. Similar to typical MPC schemes, a structured Lyapunov matrix and a distributed terminal controller, respecting the distributed structure of the system, are computed offline. However, in this scheme, a distributed positively invariant terminal set is computed online and updated at each time instant taking into consideration the current state of the system. In particular, we consider ellipsoidal terminal sets as they are easy to compute for large-scale systems. The size and the center of these terminal sets, together with the predicted state and input trajectories, are considered as decision variables in the online phase. Determining the terminal set center online is found to be useful specifically in the presence of asymmetric constraints. Finally, a relaxation of the resulting online optimal control problem is provided. The efficacy of the proposed scheme is illustrated in simulation by comparing it to a recent distributed MPC scheme with adaptive terminal sets