Distributed model predictive control of nonlinear systems with input constraints

  • Authors:

  • Jinfeng Liu;David Muñoz De la Peña;Panagiotis D. Christofides

  • Affiliations:

  • Department of Chemical and Biomolecular Engineering, University of California, Los Angeles;Departmento de Ingeniería de Sistemas y Automática Universidad de Sevilla, Sevilla, Spain;Department of Chemical and Biomolecular Engineering, University of California, Los Angeles and Department of Electrical Engineering, University of California, Los Angeles, CA

  • Venue:
  • ACC'09 Proceedings of the 2009 conference on American Control Conference
  • Year:
  • 2009

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Abstract

In this work, we introduce a distributed Lyapunov-based model predictive control method for nonlinear systems with input constraints. The class of systems considered arises naturally when new sensors, actuators and controllers are added to already operating control loops to improve closed-loop performance, taking advantage from the latest advances in sensor/actuator network 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 separate predictive controllers that compute the optimal input trajectories in a distributed manner. The proposed distributed control scheme preserves the stability properties of the Lyapunov-based controller while satisfying input constraints and improving the closed-loop performance. The theoretical results are illustrated using a chemical process example.