Roberto Ugoletti
Papers
2
Total Citations
234
H-Index
2
About
Roberto Ugoletti is a leading figure in precision motion control and iterative learning control (ILC), whose work has shaped how robotic and automated systems achieve high-accuracy repetitive tasks. His foundational research, including the highly cited "Simple learning control made practical by zero-phase filtering: applications to robotics" (154 citations), introduced a pragmatic approach to ILC by leveraging zero-phase filtering to ensure stable, convergent learning in real-world robotic applications. This work transformed ILC from a theoretical concept into a practical tool for industrial robotics. Ugoletti further advanced the field with "Discrete frequency based learning control for precision motion control" (80 citations), where he developed a unifying framework for multi-input, multi-output (MIMO) learning control. This contribution provided critical insights into the stability boundary for convergence to zero tracking error, enabling well-behaved transients during the learning process. His methods, which integrate dynamic and inverse dynamic control laws, have become essential for applications demanding sub-micron precision, such as semiconductor manufacturing and high-speed automation. Ugoletti’s work remains a cornerstone for researchers and engineers seeking robust, high-performance control in repetitive motion systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Discrete frequency based learning control for precision motion control80 citations · 2002