Project: Numerical Methods for Real-time Optimization
Work Description: Optimization based estimation and control techniques, e.g., model predictive control, can be used to optimize system performance in the presence of nonlinearities, uncertainty, and constraints. However, deploying them can be a challenge, since an optimization problem needs to be solved at each sampling instance. This is especially true for systems with limited onboard computing power such as spacecraft, aircraft, and engine control systems.
My research is focused on developing numerical algorithms specifically tailored for online optimization. In particular, I use tools from variational analysis and non-smooth calculus to design methods for solving parameterized generalized equations; a type of mathematical object which is closely related to the optimality conditions of real-time optimization problems. I also work on a variety of applications including engines, satellites, autonomous vehicles, drones, and aircraft.