Sequential and iterative architectures for distributed model predictive control of nonlinear process systems. Part I: Theory

In this work, we focus on distributed model predictive control (DMPC) of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions. The controllers are able to communicate with the rest of the controllers in making its decisions. Under the assumption that the feedback of the states of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different DMPC architectures. In the first one, the distributed controllers use a one-directional communication network, are evaluated in sequence, and each controller is evaluated only once at each sampling time; in the second one, the distributed controllers utilize a bi-directional communication network, are evaluated in parallel and iterate to improve closed-loop performance. In the design of the distributed controllers, Lyapunov-based model predictive control (LMPC) techniques are used. To ensure the stability of the closed-loop system, each controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov-based controller. We prove that the proposed DMPC architectures enforce practical stability in the closed-loop system and ensure optimal performance.