Stochastic data-driven NMPC for partially observable systems using Gaussian processes: a mineral flotation case study

Abstract

This paper presents a nonlinear model predictive control (NMPC) strategy using Gaussian Processes (GPs) to control a froth flotation process under partial observability. The GP state-space model predicts future states for both observable and latent variables, using available data, while incorporating the probability distribution of these predictions into an optimization problem. This improves robustness against measurement noise and process disturbances and evaluates the impact of feed particle size, a typical process disturbance. We assessed the framework’s ability to maintain optimal process performance across varying operating conditions. The results demonstrate that the proposed GP-MPC framework improves process efficiency, even with frequent changes in particle size and measurement noise, confirming its potential for online control of partially observable systems.