Iterative learning control
Iterative learning control refers to the mechanism by which the necessary control can be synthesized by repeated trials—based on the fundamental recognition that repeated practice is a common mode of human learning. Learning control is most suitable for operations where the same task is to be performed over and over again, e.g., robots in a manufacturing line. Available learning techniques range from those requiring no knowledge of the system dynamics to more sophisticated methods involving system identification to make the learning process efficient and successful on difficult problems. Our research finds ways to design optimal iterative learning controllers that are robust to model uncertainty, and capable of producing monotonic convergence.
Faculty contact: Minh Q. Phan