Machine Learning Modeling and Predictive Control of Nonlinear Processes
Using Noisy Data
Abstract
This work focuses on machine learning modeling and predictive control of
nonlinear processes using noisy data. We use long short-term memory
(LSTM) networks with training data corrupted by two types of noise:
Gaussian and non-Gaussian noise, to train the process model that will be
used in a model predictive controller (MPC). We first discuss the LSTM
training with noisy data following a Gaussian distribution, and
demonstrate that the standard LSTM network is capable of capturing the
underlying process dynamic behavior. Subsequently, given that the
standard LSTM performs poorly on a noisy dataset from industrial
operation (i.e., non-Gaussian noisy data), we propose an LSTM network
using Monte Carlo dropout method to reduce the over-fitting to noisy
data. Furthermore, an LSTM network using co-teaching training method is
proposed to further improve its approximation performance when clean
data from a nonlinear model capturing the nominal process state
evolution is available.