Machine Learning-Based Distributed Model Predictive Control of
Nonlinear Processes
Abstract
This work addresses the design of distributed model predictive control
(DMPC) systems for nonlinear processes using machine learning models to
predict nonlinear dynamic behavior. Specifically, sequential and
iterative distributed model predictive control systems are designed and
analyzed with respect to closed-loop stability and performance
properties. Extensive open- loop data within a desired operating region
are used to develop Long Short-Term Memory (LSTM) recurrent neural
network models with a sufficiently small modeling error from the actual
nonlinear process model. Subsequently, these LSTM models are utilized in
Lyapunov- based DMPC to achieve efficient real-time computation time
while ensuring closed-loop state boundedness and convergence to the
origin. Using a nonlinear chemical process network exam- ple, the
simulation results demonstrate the improved computational efficiency
when the process is operated under sequential and iterative DMPCs while
the closed-loop performance is very close to the one of a centralized
MPC system.