Research — Advanced Power Systems and Control Laboratory

Research

Our goal is to establish a long term research and educational program in the area of automatic control and dynamic systems. We pursue research in:

Data Attribution and Agentic AI for Control, Manufacturing, and Robotics

Objectives

To develop data attribution across the control stack, from system identification and the linear quadratic regulator to data-enabled predictive control and in-context vision-language-action (VLA) policies
To connect changes in data to certified control safety margins, and to study trajectory unlearning, group effects and cross-agent influence
To develop agents that use attribution to improve robot policies through post-training and in-context learning, and that improve their own skills, tools and harness
To evaluate the integrated loop in roll-to-roll manufacturing and robot manipulation

Objectives

To develop FEM models and control algorithms to mitigate undesired drillstring dynamics
To develop driftflux models and two-fluid models to describe the transient well hydraulic behavior, and develop novel control methodologies to mitigate the kick effects
To develop software tools to optimize directional drilling path

Objectives

To develop control strategies to maintain the optimal peeling condition for successful device transfer
To build physics-based, control-oriented, models of the R2R peeling process
To explore the applicability of advanced control techniques such as switching control and hybrid learning-based real-time optimal control on the R2R dry transfer process

Objectives

To develop a platform that can perform optimal dispatching and routing of a school of autonomous mobile robots/ground vehicles in real time considering both the shortest route and the task assignment priority
To develop a framework that design customized autonomous mobile robots/ground vehicles based

Objectives

To develop models and online estimators for predicting the state of health for both fuel cells and rechargeable batteries in real time
To develop control strategies for hybrid powertrain systems and BMS in real time
- Intercity trains
- Delivery trucks
- Buses
- Passenger vehicles

Objectives

To develop a system dynamic-based approach for drowsiness detection using electrooculography signals
To develop a bradycardia prediction algorithm based on a single ECG signal collected from preterm infants

Objectives

To develop optimal control algorithms to improve microgrid resilience with respect to voltage and frequency
To develop decentralized control themes that use wind turbine rotors for energy storage and improve the system efficiency

Objectives

To develop optimal controllers to maximize wind turbine energy capture and minimizing structure fatigue loading
To explore switched control theories to mitigate the undesired wind turbine transition dynamics between Region 2 and Region 3