Control Lyapunov-Barrier Function-Based Predictive Control of Nonlinear Systems Using Machine Learning Models

Abstract

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. As a key requirement for CLBF-based MPC is the availability of a dynamic model to predict future states and optimize control actions, this work presents a CLBF-MPC method using an ensemble of recurrent neural network (RNN) models. Guaranteed closed-loop stability and process operational safety are derived for the system with two types of unsafe regions, i.e., bounded and unbounded sets. The application of the proposed RNN-based CLBF-MPC method is demonstrated through a chemical process example with a bounded and an unbounded unsafe region, respectively.