Optimal Iterative Learning Control

Dr Bing Chu is an associate professor in Electronics and Computer Science at University of Southampton. Before joining University of Southampton in 2012, he was a postdoctoral researcher at University of Oxford (2010-2012). He teaches modules in the general physics, signals, systems and control area at undergraduate/postgraduate level. He has authored or co-authored 70 peer-reviewed scientific publications. He has been the recipient of many awards including the prestigious UKACC best paper prize and Certificate of Merit for IET Control and Automation Doctoral Dissertation Prize. He is a regular referee for a number of international journals and conferences. His current research interests include analysis and control of large scale networked systems, iterative and repetitive control, learning control, applied optimisation theory and their applications.

David H. Owens is a professor at University of Sheffield, UK and Zhengzhou University, China. He has 50 years of experience of Control Engineering theory and applications in areas including nuclear power, robotics and mechanical test. His research has included multivariable frequency domain theory and design, the theory of multivariable root loci, contributions to robust control theory, theoretical methods for controller design based on plant step data and involvement in aspects of adaptive control, model reduction and optimization-based design. His early experience of modelling and analysis of systems with repetitive dynamics originally arising in control of underground coal cutters led to substantial contributions (with collaborator E. Rogers and others) in the area of repetitive control systems (as part of 2D systems theory) but more specifically, since 1996, in the area of iterative learning control when he introduced the use of optimization to the ILC community in the form of "norm optimal iterative learning control". Since that time he has added considerable detail and depth to the approach and introducing the ideas of parameter optimal iterative learning to simplify the implementations. Applications have included industrial projects in automotive/mechanical tests and the development of data analysis tools for control of gantry robots and stroke rehabilitation equipment with collaborators at Southampton University. Professor Owens was elected a Fellow of the UK Royal Academy of Engineering for his contributions to knowledge in these and other areas.

1. Introduction to Iterative Learning Control.- 2. Brief Review of Systems Control Theory.- 3. Parameter Optimal Iterative Learning Control.- 4. Inverse Based Iterative Learning Control.- 5. Gradient Based Iterative Learning Control.- 6. Norm Optimal Iterative Learning Control.- 7. Optimal Iterative Learning Control: Constraint Handling.- 8. Accelerating the Convergence.- 9. A Case Study on a Robotic Testing Platform.- 10. Summary and Future Research Directions.