Online machine learning modeling and predictive control of nonlinear systems with scheduled mode transitions
Cheng Hu, Yuan Cao, Zhe Wu
Abstract
Abstract This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady‐state. After the system is switched to another operating region under a Lyapunov‐based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real‐time process data to improve closed‐loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed‐loop stability results are established for the switched nonlinear system under RNN‐based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady‐states is used to demonstrate the effectiveness of the proposed RNN‐MPC scheme.