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Large Scale Distributed Predictive Control


We focus on distributed control problems for large scale dynamical systems composed of loosely coupled subsystems. A set of unmanned vehicles moving in formation, cross-directional weight control in paper machines, the coordination of cameras in a monitoring network, and the coordination of wireless sensing/control devices for microclimate control in large buildings are all problems belonging to this class.

Brief Description

In the control problem the subsystems are dynamically decoupled or loosely coupled. Nevertheless, their dynamical behavior is coupled through a performance index and the interconnection constraints. Such coupling is described through a graph where each system is a node and the control action at each node is based only on local and neighboring state information. We study the analysis and the synthesis of distributed controllers depending on the structural properties of the interconnection graph topology.

We have introduced a novel methodology for the synthesis of distributed controllers which takes explicitly into account the interconnection constraints and uses the model of the neighbors to predict their behavior. Stability conditions have been derived. Such conditions (i) highlight the role of prediction errors between neighbors in the stability of the aggregate system, (ii) are local to each node and depend only on neighboring nodes that can be reached trough at most two edges, thus leading to a complexity reduction for interconnection graphs of large diameter, and (iii) help understand the importance of information exchange between neighbors and its role in stabilizing the entire system.

Current Research

The proposed distributed framework provides an attractive and simple way for synthesizing distributed controllers. We are currently studying
  • The analysis and the synthesis of coordination rules for improving the feasibility domain of the distributed control law.
  • The interplay between graph topology, stability, performance and robustness to disturbance propagations.

100 4th order dynamical systems controlled by distributed MPC and fixed communication

6 vehicles in reconfiguration,exchanging predictions with a time varying communication


Two vehicles exchanging predictions without coordination rules

Two vehicles exchanging predictions with coordination rules