On-line Process Physics Tests via Lyapunov-based Economic Model Predictive Control and Simulation-Based Testing of Image-Based Process Control
Next-generation manufacturing involves increasing use of automation and data to enhance process efficiency. An important question for the chemical process industries, as new process systems (e.g., intensified processes) and new data modalities (e.g., images) are integrated with traditional plant automation concepts, will be how to best evaluate alternative strategies for data-driven modeling and synthesizing process data. Two methods which could be used to aid in this are those which aid in testing data-based techniques on-line, and those which enable various data-based techniques to be assessed in simulation. In this work, we discuss two techniques in this domain which can be applied in the context of chemical process control, along with their benefits and limitations. The first is a method for testing data-driven modeling strategies on-line by postulating the experimental conditions which could reveal if a model is correct, and then attempting to collect data which could help to reveal this. The second strategy is a framework for testing image-based control algorithms via simulating both the generation of the images as well as the impacts of control on the resulting systems.
Controls and Control Theory | Information Security | Process Control and Systems
H. Oyama, A. F. Leonard, M. Rahman, G. Gjonaj, M. Williamson and H. Durand, "On-line Process Physics Tests via Lyapunov-based Economic Model Predictive Control and Simulation-Based Testing of Image-Based Process Control," 2022 American Control Conference (ACC), 2022, pp. 2479-2484, doi: 10.23919/ACC53348.2022.9867435.
Controls and Control Theory Commons, Information Security Commons, Process Control and Systems Commons
Financial support from the National Science Foundation CBET-1839675 and CNS-1932026, Air Force Office of Scientific Research award number FA9550-19-1-0059, and Wayne State University, is gratefully acknowledged. This paper was published at the 2022 American Control Conference (ACC), https://doi.org/10.23919/ACC53348.2022.9867435