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 50 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:
Title: Vision and Wireless-based Surveying for Intelligent OSAM Navigation (VISION)
Period: Aug 12, 2022 -- January 11, 2023
Sponsor: SpaceWERX through Modularity Space
Title: Reconfigurable Guidance and Control Systems for Emerging On-Orbit Servicing, Assembly, and Manufacturing (OSAM) Space Vehicles
Period: July 5, 2022 -- Dec 5, 2022
Sponsor: SpaceWERX through ControlX
Title: Resilient Distributed Control of Multiple Spacecraft for Future Cooperative Space Exploration
Period: Aug 30, 2021 -- Dec 31, 2022
Sponsor: Florida Space Grant Consortium as funded by the National Aeronautics and Space Administration
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