摘要：AbstractIn this work, a real-time Control Lyapunov-Barrier Function-based model predictive control (CLBF-MPC) system using recurrent neural network (RNN) models is developed for a general class of nonlinear systems to ensure closed-loop stability and operational safety accounting for time-varying disturbances. An RNN model is first constructed for the nominal system (i.e., without disturbances) and utilized in the design of CLBF-MPC to provide state prediction. Subsequently, to improve the closed-loop performance in terms of operational safety and stability in the presence of disturbances, online learning of RNN models is incorporated within the real-time implementation of CLBF-MPC to update the RNN models using the most recent process measurement data. The proposed adaptive machine-learning-based CLBF-MPC method is evaluated using a nonlinear chemical process example.
