Output details
13 - Electrical and Electronic Engineering, Metallurgy and Materials
University of Southampton
Iterative learning control for a non-minimum phase plant based on a reference shift algorithm
Significance of output:
Develops novel iterative learning control framework requiring no underlying model, and establishes 100-fold performance increase on standard industrial schemes. Full stability analysis is provided, together with rigorous experimental evaluation. Represents large body of research (15 journal papers and 17 conference papers) on related topics and constitutes a major advance in high-accuracy automation. The methodology has been directly used in industrial processes, including linear particle accelerators at DESY (in collaboration with U. Hamburg) and Kuka commercial robotics. The technology has been applied by leading groups (Missouri University, U. Illinois, KU Leuven, Berkley) to gantry robotics, CNC machines, power electronics and quadrotors.