Decentralized machine-learning-based predictive control of nonlinear processes

Abstract

This work focuses on the design of decentralized model predictive control (MPC) systems for nonlinear processes, where the nonlinear process is broken down into multiple, yet coupled subsystems and the dynamic behavior of each subsystem is described by a machine learning model. One decentralized MPC is designed and used to control each subsystem while accounting for the interactions between subsystems through feedback of the entire process state. The closed-loop stability of the overall nonlinear process network and the performance properties of the decentralized model predictive control system using machine-learning prediction models are analyzed. More specifically, multiple recurrent neural network models suited for each different subsystem need to be trained with a sufficiently small modeling error from their respective actual nonlinear process models to ensure closed-loop stability. These recurrent neural network models are subsequently used as the prediction model in decentralized Lyapunov-based MPCs to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. The simulation results of a nonlinear chemical process network example demonstrate the effective closed-loop control performance when the process is operated under the decentralized MPCs using the independently-trained recurrent neural network models, as well as the improved computational efficiency compared to the closed-loop simulation of a centralized MPC system.