Data-driven iterative learning control: achieving model-free convergence for real-world systems

This PhD project at the University of Southampton, supervised by Professor Bing Chu and Professor Paolo Rapisarda, focuses on advancing iterative learning control (ILC) methodologies by removing the reliance on analytical models. Analytical models are often expensive or impractical to obtain, and this research aims to develop model-free approaches to ILC, which could have significant implications for automation, robotics, and control systems engineering. The project is ideal for candidates interested in control theory, robotics, and engineering applications where traditional modeling is challenging. The supervisory team brings expertise in control systems and iterative learning, providing a strong foundation for research and development in this area. The position description does not specify funding details or entry requirements, so interested applicants should consult the university's official PhD admissions page for further information. The application deadline is not provided.