Statistical machine‐learning–based predictive control of uncertain nonlinear processes. Issue 5 (1st March 2022)

• Record Type: Journal Article
• Authors: Wu, Zhe; Alnajdi, Aisha; Gu, Quanquan; Christofides, Panagiotis D.
• Is Part Of: AIChE journal. Volume 68:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-01
• Subjects: generalization error -- machine learning -- model predictive control -- recurrent neural networks -- stochastic nonlinear systems; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.17642
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: In this study, we present machine‐learning–based predictive control schemes for nonlinear processes subject to disturbances, and establish closed‐loop system stability properties using statistical machine learning theory. Specifically, we derive a generalization error bound via Rademacher complexity method for the recurrent neural networks (RNN) that are developed to capture the dynamics of the nominal system. Then, the RNN models are incorporated in Lyapunov‐based model predictive controllers, under which we study closed‐loop stability properties for the nonlinear systems subject to two types of disturbances: bounded disturbances and stochastic disturbances with unbounded variation. A chemical reactor example is used to demonstrate the implementation and evaluate the performance of the proposed approach.