Human-Machine Interaction

With increasing automation in all aspects of society, humans are being tasked with interacting, and in some cases collaborating, with autonomous systems in a variety of contexts. At the core of successful cooperation between man and machine is trust, and we are fundamentally interested in mathematically modeling how human trust evolves during interactions with automated systems and how feedback control principles can be applied to help machines better collaborate with humans.

We have published several articles over the last few years on this topic that consider novel ways of estimating trust via psychophysiological sensing (such as galvanic skin response (GSR) and electroencephalography (EEG)) as well as using behavioral data. We have additional demonstrated, experimentally, the use of dynamic machine transparency for improving a context-based performance metric. We collaborate with researchers in the REID Lab here at Purdue. We continue to investigate new methods for estimating trust, and other relevant cognitive states, in such a way that we can ultimately design better autonomous systems for improving humans’ quality of life.

Graduate Research Assistant(s): Kumar Akash

Dynamic Modeling and Control of Time-Delayed Systems

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

Dynamic Modeling, Design, and Control for Improved (Thermal) Energy Management

Herrick Labs micro-CHP Test Bed

A research emphasis in the Jain Research Lab is on the intersection of control algorithms and thermal energy systems, defined here very broadly to include any system in which thermal energy plays a key role in the operation of the system. We have several projects related to this topic, ranging from the integration of thermo-chemical energy storage into residential heating and cooling systems to advanced control strategies for micro-combined heat and power systems aimed at increasing the robustness and efficiency of our domestic electricity supply. We are collaborating with researchers outside of Purdue to model how phase change materials integrated into heat exchangers can ultimately be used to improve the dynamic response of such components when integrated into cooling systems.

Graduate Research Assistant(s): Trevor Bird, Karan Gohil, Austin Nash

Dynamic Modeling and Control in Advanced Transportation Systems

More recently we have started investigating the role of advanced control and autonomy in future transportation systems. Through collaborations with other faculty here at Purdue and industry partners, we are designing new algorithms for platooning of heavy duty Class 8 trucks. We are also tackling large-scale modeling of the freight transportation network by considering it as a system of systems, to answer questions like “what incentives and policies, deployed over time in what way, will lead to a significant increase in the adoption of battery electric trucks?”

Graduate Research Assistant(s): Ana Guerrero de la Peña