The growing complexity of dynamical systems results in unavoidable discrepancies between mathematical models and real physical systems. These complexities result in the degradation of control system performance. Therefore, developing control system design architectures for stable and robust performance that satisfy a wide array of real-world constraints is a challenging task. Specifically, our research specializations (alphabetically) include adaptive systems and learning, autonomy, flight dynamics and control, linear control systems, multiagent systems, nonlinear control systems, optimization and optimal control systems, robotics, and robust control systems. In these research areas, we have co-authored more than 60 journal and conference publications. In addition, in the dynamical systems and control field, it is obvious that theoretical study and experimentation provide guidelines for each other and are indispensable ingredients for new real-world control problems. To this end, the main characteristic of our research is rigorous theoretical treatment of challenges relevant to academia, industry, and government. In our research, we have also put a special emphasis on including both high-fidelity simulations and experimental validations of the new theoretical approaches that we have developed. In particular, the theoretical results that we have developed to date have been implemented to identify and/or control several electrical, robotic, and mechanical systems.

Externally Funded projects:

Period: May 15, 2023 -- July 31, 2023

Sponsor: AFWERX through ControlX

Period: February 16, 2023 -- May 21, 2024

Sponsor: Department of the Air Force (DAF) through ControlX

Period: February 1, 2023 -- January 31, 2023

Sponsor: FAA 

Period: Aug 12, 2022 -- January 11, 2023

Sponsor: SpaceWERX through Modularity Space

Period: July 5, 2022 -- Dec 5, 2022

Sponsor: SpaceWERX through ControlX

Period: Aug 30, 2021 -- Dec 31, 2022

Sponsor: Florida Space Grant Consortium as funded by the National Aeronautics and Space Administration

 Selected Researches

Change in the real part of the maximum eigenvalue of matrix A with respect to the changes in w (uncertainty) and m (actuator bandwidth). The line color variations from blue to red indicate the direction that m is increased from 0 to 1. Note that stability of the controlled system is guaranteed when max(Re(λi (A)))< 0.

Nominal (blue) vs Proposed Model Reference Control (green) Laws Cart Tracking Result with Unmodeled Dynamics.

Related Research Directions for Uncertain Sole Systems

Related Research Directions for Uncertain Multiagent Systems