Distributed model predictive control system design using lyapunov techniques

Abstract

In this work, we introduce a distributed control method for nonlinear process systems in which two different controllers have to coordinate their actions to regulate the state of the system to a given equilibrium point. This class of systems arises naturally when new sensors, actuators and controllers are added to already operating control loops to improve closed-loop performance and fault tolerance, taking advantage from the latest advances in wireless technology. Assuming that there exists a Lyapunov-based controller that stabilizes the closed-loop system using the pre-existing control loops, we propose to use Lyapunov-based model predictive control to design two different predictive controllers that coordinate their actions in an efficient fashion. Specifically, the proposed distributed control design preserves the stability properties of the Lyapunov-based controller, improves the closed-loop performance and is computationally more efficient compared to the corresponding centralized MPC design. The theoretical results are demonstrated using a chemical process example. © 2009 Springer Berlin Heidelberg.