Journal Paper: Distributed model predictive control of linear systems with persistent disturbances

Paul Trodden and Arthur Richards, Distributed model predictive control of linear systems with persistent disturbances, International Journal of Control, Online, published 24th June 2010


Abstract This article presents a new form of robust distributed model predictive control (MPC) for multiple dynamically decoupled subsystems, in which distributed control agents exchange plans to achieve satisfaction of coupling constraints. The new method offers greater flexibility in communications than existing robust methods, and relaxes restrictions on the order in which distributed computations are performed. The local controllers use the concept of tube MPC - in which an optimisation designs a tube for the system to follow rather than a trajectory - to achieve robust feasibility and stability despite the presence of persistent, bounded disturbances. A methodical exploration of the trades between performance and communication is provided by numerical simulations of an example scenario. It is shown that at low levels of inter-agent communication, distributed MPC can obtain a lower closed-loop cost than that obtained by a centralised implementation. A further example shows that the flexibility in communications means the new algorithm has a relatively low susceptibility to the adverse effects of delays in computation and communication.

Keywords: linear systems; distributed control; constrained control

DOI: 10.1080/00207179.2010.485280

Flight Control... Automated

We tried out one of our routing algorithms on a version of the game Flight Control.  When each new aircraft enters, a Mixed Integer Linear Program (MILP) optimization finds its best route to its target, avoiding the routes already set for other aircraft.  So far its best score is 165.  It tends to fail when aircraft don't quite follow the route they've been set, or if two get close to the runway entrance at the same time.  We're working on improving performance, but there's a catch: the more detail you put in the optimizer, the slower it responds.  That puts aircraft further off their assigned paths and leads to problems later on. Thanks to Colin for coding the game.