Over the last two decades, synchronization has been a popular subject in multi-agent systems because of its wide array of applications ranging from flight formation to energy networks. From a control theory viewpoint, how should distributed controllers (i.e., local interactions between the agents and control protocols) be structured to ensure synchronization? We develop scalable analysis and design methods with performance guarantees under heterogeneities and uncertainties. 

Principal Investigator: Burak Sarsilmaz

Modern aircraft are equipped with bio-inspired features such as morphing wings and rotating tails. These features present new challenges for stability, trajectory planning and tracking, and vibration suppression. We bring the latest advances in control to these applications. Of particular interest are nonlinear, linear parameter varying, neural network, and data driven control techniques. Check out our VTOL aircraft flight test video.

Principal Investigators: Matt Harris, Tianyi He

The design of realistic spacecraft trajectories must account for nonlinear dynamical models, actuator constraints, and computational limits of onboard computing. The dynamics and constraints present themselves in nonconvex ways, but computational guarantees needed for onboard use demand convex formulations. We develop novel optimization formulations suitable for efficient, provable algorithms. 

Principal Investigators: Matt Harris, Burak Sarsilmaz

In the ever-evolving field of robotics, one of the most critical and intellectually demanding areas of focus is decision-making algorithms. The intricacies of enabling a machine to make autonomous, context-aware decisions in complex and unpredictable environments are immense. This research often involves the confluence of multiple disciplines, from artificial intelligence and machine learning to control systems.

Principal Investigators: Mario Harper, Greg Droge, Tianyi He

From aircraft collision avoidance to wildfire management and disaster response, decision-making under uncertainty is a crucial factor.  Research is conducted in many domains ranging from financial optimizations, transportation, and policy implications.  Computational methods and new algorithms are developed to understand, learn from, and effectively respond in situations where there is little to no data and events happen quickly.

Principal Investigator: Mario Harper

Financial Technology is an arena of growing importance as digital transformation reshapes traditional financial systems. We are investigating the efficacy and fairness of novel financial instruments and working on disparate impact analysis to help ensure that new financial technologies are equitable. In collaboration with partners in academia and the banking sector, we are examining how fintech can provide innovative solutions to longstanding challenges in financial services. 

Principal Investigator: Mario Harper

Uncertainty propagation is an important aspect of navigation, orbit determination, and robust mission design in the study of astrodynamics. The high degree of nonlinearity present in cislunar dynamics and resulting non-Gaussian distributions pose interesting challenges for traditional uncertainty propagation techniques. We are studying existing and novel analytical and computational methods for understanding how uncertainty propagates in this challenging environment. 

Principal Investigator: Jackson Kulik

Many satellites require propulsion to complete their missions. We are interested in improving the state of the art in accurately characterizing and performing estimation and uncertainty quantification on maneuvers and other thrusting events. We are also interested in efficiently and realistically implementing maneuvers that were originally optimized under less realistic assumptions. 

Principal Investigator: Jackson Kulik