(222h) Evaluating Controllers for Next-Generation Manufacturing

The next generation of manufacturing for the chemical and materials industries involves a variety of new considerations for which it will be necessary to develop good strategies for evaluating how controllers behave. For example, with cybersecurity of control systems as an important concern [1] with increasing emphasis on data and automation, it is important to consider how process design and control interact in making processes more resilient to attacks. Furthermore, the next generation of materials considerations may involve not only new control methods that might, for example, take advantage of image-based control, but also potentially materials that are themselves controlled. Developing techniques for evaluating these various types of considerations computationally to reduce testing efforts while verifying safe and on-specification operation is important.

This talk will review a series of case studies where we explore ideas for evaluating control for next-generation manufacturing processes as diverse as geothermal energy production, self-assembly with an assumed image-based sensor, and materials that respond to changes in their environment. In the first part of the talk, we discuss how steady-state simulation studies of a geothermal energy process can guide an understanding of how different designs for a geothermal energy system contribute to the severity of different types of cyberattacks on the control systems in the sense of how much they impact the production of electricity [2]. We discuss how dynamic simulation might contribute to further analysis. We provide thoughts on how attacks seeking to impact manufacturing could also have impact by modifying peoples’ behavior. We close by presenting a perspective on how next-generation materials design and manufacturing can be impacted by new concepts for testing and understanding control for such systems. For example, inspired by the concept that we might use the software Blender for image-based controller evaluation [3,4], we discuss how more sophisticated examples of image-based control than were previously presented in [3,4] might be considered. For example, inspired by cases where image processing has been integrated with self-assembly control [5], we discuss preliminary steps that would need to be taken if one wants to build an image-based control simulation for such a process in Blender to test the effectiveness of various image-based control laws without an experimental unit. Finally, we present thoughts related to the control of materials, particularly how control theory intersects with modeling strategies such as molecular dynamics. While control for molecules at the quantum level has been of interest [6], we discuss molecular dynamics simulation [7] due to its closer framework to the traditional ordinary differential equations models used in control theory for chemical processes.

[1] McLaughlin, S., Konstantinou, C., Wang, X., Davi, L., Sadeghi, A. R., Maniatakos, M., & Karri, R. (2016). The cybersecurity landscape in industrial control systems. Proceedings of the IEEE, 104(5), 1039-1057.

[2] Rangan, K. K., J. Abou Halloun, H. Oyama, I. Azali Assoumani, N. Jairazbhoy, H. Durand, and S. K. Ng, “Design and Control Resilience/Robustness: Relationships to Quantum Computing and Cybersecurity,” Proceedings of the IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS), Busan, Republic of Korea, in press.

[3] Oyama, H., D. Messina, R. O'Neill, S. Cherney, M. Rahman, K. K. Rangan, G. Gjonaj, and H. Durand, “Test Methods for Image-Based Information in Next-Generation Manufacturing,” Proceedings of the IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS), Busan, Republic of Korea, in press.

[4] Oyama, H., 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,” Proceedings of the American Control Conference, Atlanta, Georgia, in press.

[5] Grover, M. A., Griffin, D. J., Tang, X., Kim, Y., & Rousseau, R. W. (2020). Optimal feedback control of batch self-assembly processes using dynamic programming. Journal of Process Control, 88, 32-42.

[6] Magann, A. B., Grace, M. D., Rabitz, H. A., & Sarovar, M. (2021). Digital quantum simulation of molecular dynamics and control. Physical Review Research, 3(2), 023165.

[7] Rapaport, D. C., & Rapaport, D. C. R. (2004). The Art of Molecular Dynamics Simulation. Cambridge University Press.