Artificial Neural Network-Based Model Predictive Control Using Correlated Data

Abstract

This work addresses the problem of implementing model predictive control (MPC) in situations where the training data available for modeling contains possible correlations, and an artificial neural network (ANN)-based model is being used. In particular, we consider a problem where data sets are collected from a process that operates under the closed-loop condition in which correlations are induced between several input and output variables. In this situation, if the correlation problem is not addressed, manipulated inputs (calculated by MPC without considering the specific correlation in the input space) and independently prescribed set-points may require predictions in regions where the model is not trained, resulting in a poor closed-loop performance. To address this issue, principal component analysis (PCA)-based strategies are applied to both the input and output spaces in a way that maintains model validity. To that end, a new constraint on the squared prediction error (SPE) is incorporated into the ANN-based MPC optimization problem to make control actions follow the PCA model built using the training input data. Next, a PCA model is developed using the training output data, and then an optimization problem subject to the SPE constraint is defined to calculate set-points which are achievable. The effectiveness of the proposed ANN-based MPC to track these set-points is demonstrated using a chemical reactor example. Finally, a new autoencoder-based strategy is proposed to compute the achievable set-points. This is performed by replacing the PCA-based constraint with the autoencoder-based constraint in the optimization problem to calculate the set-points. The results indicate that the ANN-based MPC performance is improved when the autoencoder-based set-points are used.