Statistical machine-learning-based predictive control of uncertain nonlinear processes

doi:10.1002/aic.17642

Statistical machine-learning-based predictive control of uncertain nonlinear processes

2022-03-01

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Panagiotis D. Christofides *

DOI：10.1002/aic.17642

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Abstract

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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.

Keywords:

generalization error

machine learning

model predictive control

recurrent neural networks

stochastic nonlinear systems

Statistical machine-learning-based predictive control of uncertain nonlinear processes

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