Eric Kerrigan Email & Phone Number

My interest is in the development of theory and methods for model predictive control (MPC) to handle nonlinearities and uncertainties in a systematic fashion. In MPC, a sequence of optimal control and estimation problems need to be solved in real-time - this requires orders of magnitude more computational resources than most other control methods. My particular area of expertise is in the design of efficient numerical methods and embedded computing architectures for solving these optimal control and estimation problems in real-time.I am also interested in developing new multi-objective optimization methods for the design of the overall closed-loop system. I am particularly interested in exploring how the complexities of the algorithms, computing architecture and physical realization need to be traded off to satisfy given system-wide performance specifications.I have a joint appointment in the Department of Electrical & Electronic Engineering and the Department of Aeronautics. My theoretical research is therefore motivated by a wide variety of problems in the design of aerospace, renewable energy and information systems. Applications include scheduling of computation and communication in aerial and mobile robotic networks, aerodynamic drag reduction, gust and load alleviation in wind turbine blades and control of space vehicles.

Listed skills include Dynamical Systems, Numerical Analysis, Mathematical Modeling, Optimization, and 30 others.