An iterative decentralized MPC algorithm for large-scale nonlinear systems

Abstract

This paper presents a robust stabilizing decentralized model predictive control (MPC) algorithm for discrete-time nonlinear large-scale systems comprised of multiple subsystems. Each subsystem is controlled with a tube-based model predictive control algorithm that renders the subsystem input-to-state stable (ISS) with respect to both the external disturbances affecting it and the interconnections to the other subsystems (treated as additional disturbance inputs). The algorithm entails redesigning the MPC control strategy periodically, taking into account newly acquired information regarding the interconnection uncertainty sets. By using a regional version of the small-gain theorem for multiple nonlinear systems, we then show that the overall closed-loop system is ISS with respect to the external disturbances.