2022/05/09 by Ryan McCloy, McCloy, Ryan, Wei Lai +3
Engineering · #Advanced Control Systems Optimization #Carbon Dioxide Capture Technologies #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Process Optimization and Integration #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2205.04033
openalex publication_date 2022/05/09 · openalex created_date 2022/11/27 · openalex updated_date 2026/07/28
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.