Statistical machine-learning-based predictive control using barrier functions for process operational safety. (July 2022)

• Record Type: Journal Article
• Authors: Chen, Scarlett; Wu, Zhe; Christofides, Panagiotis D.
• Is Part Of: Computers & chemical engineering. Volume 163(2022)
• Publication Date: 2022-07
• Subjects: Neural networks -- Generalization error -- Nonlinear model predictive control -- Process operational safety -- Barrier functions -- Statistical machine learning; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2022.107860
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Machine learning-based construction of barrier functions. MPC design using barrier functions. Closed-loop stability and safety in probability. Evaluation of MPC performance, stability and safety. Abstract: In this work, we present statistical model predictive control with Control Lyapunov-Barrier Functions (CLBF) built using machine learning approaches, and analyze closed-loop stability and safety properties in probability using statistical machine learning theory. A feedforward neural network (FNN) is used to construct the Control Barrier Function, and a generalization error bound can be obtained for this FNN via the Rademacher complexity method. The FNN Control Barrier Function is incorporated in a CLBF-based model predictive controller (MPC), which is used to control a nonlinear process subject to input constraints. The stability and safety properties of the closed-loop system under the sample-and-hold implementation of FNN-CLBF-MPC are evaluated in a statistical sense. We use a chemical process example to demonstrate the relation between various factors of building an FNN model and the generalization error, as well as the probabilities of closed-loop safety and stability for both bounded and unbounded unsafe sets.