Making Non-Centralized a Model Predictive Control Scheme by Using Distributed Smith Dynamics ?

This paper proposes a non–centralized Model Predictive Control (MPC) scheme for a system comprised by several sub-systems. Operational constraints for each sub–system are considered as well as a single coupled constraint on the control inputs that models a limitation of the resource supplied by the controller. If the underlying optimization problem is of largescale nature, traditional MPC suffers from computational burden issues. A cause of this problem is the requirement of having centralized information to guarantee that the computed control actions satisfy the coupled constraint. In this work, a traditional MPC is made non–centralized by means of a strategy based on distributed population dynamics. The proposed methodology divides the problem into several local MPC controllers that coordinate their decisions by using a communication network without the need of a centralized scheme. It is proved that this methodology provides an optimal solution that satisfies both the operational constraints of each sub–system, and the coupled constraint of the control signals. Finally, the proposed method is compared with a traditional centralized MPC in an industrial problem that involves several continuously stirred tank reactors.