Control Lyapunov-Barrier function-based predictive control of nonlinear processes using machine learning modeling

摘要

Control Lyapunov-Barrier functions (CLBF) have been adopted to design model predictive controllers (MPC) for input-constrained nonlinear systems to ensure closed-loop stability and process operational safety simultaneously. In this work, a CLBF-MPC using an ensemble of recurrent neural network (RNN) models is proposed with guaranteed closed-loop stability and process operational safety for two types of unsafe regions, i.e., bounded and unbounded sets, for nonlinear processes. The application of the proposed RNN-based CLBF-MPC method is demonstrated through a chemical process example.