A Contraction-constrained Model Predictive Control for Nonlinear Processes using Disturbance Forecasts

TL;DR

This paper introduces a contraction-constrained model predictive control approach that leverages disturbance forecasts to enhance control performance and stability in nonlinear processes, balancing economic optimization and stabilization.

Contribution

It proposes a novel contraction-constrained MPC method that guarantees stability and improves economic performance using disturbance forecasts in nonlinear process control.

Findings

Enhanced control stability with disturbance forecasts

Improved economic efficiency in process control

Validated effectiveness on nonlinear process models

Abstract

Model predictive control (MPC) has become the most widely used advanced control method in process industry. In many cases, forecasts of the disturbances are available, e.g., predicted renewable power generation based on weather forecast. While the predictions of disturbances may not be accurate, utilizing the information can significantly improve the control performance in response to the disturbances. By exploiting process and disturbance models, future system behaviour can be predicted and used to optimise control actions via minimisation of an economical cost function which incorporates these predictions. However, stability guarantee of the resulting closed-loop system is often difficult in this approach when the processes are nonlinear. Proposed in the following article is a contraction-constrained predictive controller which optimises process economy whilst ensuring stabilisation to operating targets subject to disturbance measurements and forecasts.