Yi Cao, Cranfield University

Recent Development on Global Self-Optimizing Control

Self-optimizing control (SOC) aims to select a set of controlled variables (CVs) for process plants subject to various uncertainties and disturbances such that when these CVs selected are controlled at constant set-points the corresponding plant operation is optimal or near optimal in terms of a predetermined economic objective. Comparing with standard real-time optimization (RTO) strategies, SOC is much simpler for implementation and does not suffer from long time waiting for steady-state convergence. Due to the feedback nature, SOC is much more robust comparing to the so called economic MPC solutions appearing recently.

Since SOC was proposed by Skogestad in 2000, most research works focused on linear model based solutions. Due to linearization, these solutions are only valid locally around the reference operating point, hence was refereed as local SOC. Recently, several approaches have been proposed to select CVs for the entire operation space. These methods are referred to global SOC (gSOC).

In the talk, the principles and algorithms of these gSOC approaches will be explained. It includes gradient regression based approaches, CV adaptation approaches, optimal operation data based approaches, subset measurement selection approach and retrofit SOC approach.

Presentation slides


Yi Cao is a Reader in Control Systems Engineering, Cranfield University. He Obtained PhD in Control Engineering from the University of Exeter in 1996, MSc in Industrial Automation from Zhejiang University, China in 1985. His main research interest is in developing systematic approaches to solve various operational problems involved in industrial processes using both models and data. Dr Cao is the main inventor of the Inferential Slug Control technology to mitigate slugging of multiphase flow in offshore oil and gas production systems. A successful field trial has showed that the technology was able to increase oil production by 10%. This achievement received the Innovation Award from the East England Energy Group (EEEgr) in 2010. His recent research is focusing on data driven self-optimizing control methodology. By applying it to water flooding process for enhanced oil recovery, it can achieve near optimal operation in spite of the uncertainties of oil reservoirs. Yi Cao has published over 150 research papers in international journals and conferences. His research work has been awarded the best application paper prize by the Control Engineering Practice in 1996 and the best oral presentation prize in the IFAC 9th IFAC Symposium on Advanced Control of Chemical Processes in 2015.

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