Our interests in controlling energy-intensive systems has led to research that addresses, in part, new algorithms and tools for controlling time-delayed systems.
Twin-roll steel strip casting produces steel strips by pouring steel directly onto rollers and compressing it to a thickness near the final gauge, whereas traditional casting uses a mold to form a steel slab that is later rolled to the desired thickness. The twin roller method is 9 times more energy efficient and 7000 times faster(!) than thick slab casting. However, achieving precise physical properties along the length of the strip poses a challenging engineering problem due to the highly coupled nature of the thermal and mechanical dynamics. We developed a new algorithm, based on iterative learning control, which can compensate for time delays that are longer than a single iteration of the casting process. We recently filed a patent with an industry partner on our algorithm, and continue to tackle exciting controls challenges in this advanced manufacturing process.
We are also developing new algorithms for plant and control co-design of thermal-fluid systems which involves consideration of how fluid-driven time delays affect the dynamic response of a system. In our work funded by the Office of Naval Research, we are answering questions like “how do we design aircraft thermal management systems that are capable of dissipating highly transient heat loads with guarantees on the system’s robustness?”
Graduate Research Assistant(s): Rian Browne, Austin Nash