Neural Network-Based Model Predictive Control of a Paste Thickener over an Industrial Internet Platform

Abstract

This work presents a real implementation of a neural-network-based model predictive control scheme (NNMPC) to control an industrial paste thickener. The implementation is done over an IIoT platform designed using the seven layer reference model for IIoT systems. Modeling is achieved using an encoder-decoder with attention recurrent neural network (RNN), while MPC search is done using particle swarm optimization (PSO). An industrial evaluation is presented, which highlights the set-point tracking and disturbance rejection capabilities of the proposed NNMPC technique.