Adaptive Quasi-Decentralized MPC of Networked Process Systems

Abstract

This work presents a framework for quasi-decentralized model predictive control (MPC) design with an adaptive communication strategy. In this framework, each unit of the networked process system is controlled by a local control system for which the measurements of the local process state are available at each sampling instant. And we aim to minimize the cross communication between each local control system and the sensors of the other units via the communication network while preserving stability and certain level of control system performance. The quasi-decentralized MPC scheme is designed on the basis of distributed Lyapunov-based bounded control with sampled measurements and then the stability properties of each closed-loop subsystem are characterized. By using this obtained characterization, an adaptive communication strategy is proposed that forecasts the future evolution of the local process state within each local control system. Whenever the forecast shows signs of instability of the local process state, the measurements of the entire process state are transmitted to update the model within this particular control system to ensure stability; otherwise, the local control system will continue to rely on the model within the local MPC controller. The implementation of this theoretical framework is demonstrated using a simulated networked chemical process.