Human-Automation Interaction

Medium-fidelity driving simulator for HMI research

With increasing automation in all aspects of society, humans are being tasked with interacting and collaborating with autonomous systems in a variety of contexts. At the core of successful cooperation between human and machine is trust. To that end, we are interested in mathematically modeling how human trust, and other human behaviors, evolve during interactions with automated systems and how feedback control principles can be applied to achieve the promise of automation---improving human quality of life.

Through collaborations with the REID Lab here at Purdue, we have published several articles on this topic that consider novel ways of estimating human trust via psychophysiological sensing (such as galvanic skin response (GSR) and electroencephalography (EEG)) as well as using behavioral data. More recently we extended our modeling to include human workload, and we have demonstrated, experimentally, the use of adaptive user interface (UI) transparency for improving human performance when assisted by an automated decision aid.

We have several ongoing projects in our group that build on our foundational work in this area. This includes human cognitive state-based control for automation designed to assist humans with skill building and modeling of human trust across multiple timescales. We are also studying how human operators make decisions in process manufacturing and using machine learning techniques to assess how automation, combined with operator expertise, can achieve better, and more consistent performance.

Graduate Research Assistant(s): Matthew Konishi, Jianqi Ruan, Katie Williams and Madeleine Yuh

Recent Research Poster(s): ►Reimagining Human Machine Interactions Through Trust Based Feedback

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

PEM fuel cell micro-CHP testbed
1/10th scale aircraft thermal management system testbed

A research emphasis in the Jain Research Lab is on design and control of 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 ongoing projects that focus both on system level dynamics as well as those of individual thermal-fluidic components, such as heat exchangers embedded with phase change materials (PCMs). The latter is a collaboration with researchers outside Purdue to model how PCMs integrated into heat exchangers can ultimately be used to improve the dynamic response of safety-critical cooling systems.

At the systems level, we are developing new algorithms for control co-design that consider the differing model fidelity needed for design optimization versus control synthesis, especially for thermodynamic systems with stringent performance requirements. We are also designing advanced control strategies for micro-combined heat and power (micro-CHP) systems aimed at increasing the robustness and efficiency of our domestic electricity supply. Finally, we are tackling improved performance and efficiency of residential cooling and heating systems through the integration and management of thermo-chemical energy storage.

Graduate Research Assistant(s): Trevor Bird and Marcin Glebocki

Iterative Learning Control for 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 were recently awarded a patent with an industry partner for our algorithm, and continue to tackle exciting controls challenges in this advanced manufacturing process.

Recent Research Poster(s): ►Iterative Learning Control for Time-Delayed and Time-Varying Systems

Dynamic Modeling and Control in Advanced Transportation Systems

Purdue-owned Class 8 trucks for platooning research

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?”

Recent Research Poster(s): ►Powering What’s Next in Freight Transportation