A Full-Day Pre-Congress Workshop for 2026 IFAC World Congress
Learning from Data: Principles, Methods, and Emerging Ideas & Applications in Iterative Learning Control
Sunday, 23 August 2026
Busan, Republic of Korea
Organizers:
Professor Kevin L. Moore, Colorado School of Mines, USA
Professor Bing Chu, University of Southampton, UK
Professor Tom Oomen, Eindhoven University of Technology, The Netherlands
Professor Ying Tan, University of Melbourne, Australia
Professor Kira Barton, University of Michigan, USA
Workshop Outline:
Data-driven learning control has emerged as a powerful framework for achieving high-performance operation in complex, uncertain, and repetitive systems. By exploiting historical data, system models, and online measurements, such controllers can adapt, optimise, and progressively improve performance over time. Iterative learning control (ILC) and related approaches have been successfully applied across a wide range of domains, including robotics, precision manufacturing, autonomous systems, chemical process control, and rehabilitation, particularly in settings where accurate system models are difficult to obtain and the tasks are inherently repetitive.
This full-day workshop aims to provide participants with a comprehensive overview of the principles, design methods, and emerging applications in data-driven learning control. The workshop will:
Introduce the fundamentals and historical development of iterative learning control and data-driven control.
Present state-of-the-art methodologies, including optimization-based design, frequency-domain approaches, norm-optimal discrete repetitive processes, learnability, and persistence of excitation.
Cover spatial and distributed ILC techniques and their practical implementations.
Showcase real-world applications through case studies and industrial examples.
Provide dedicated discussion sessions for open challenges, emerging directions, and potential collaborations.
By the end of the workshop, participants will have a clear understanding of data-driven learning control frameworks, the ability to connect theoretical concepts with practical applications, and insight into future research opportunities in this rapidly evolving field.
Desired Learning Outcomes:
By the end of the workshop, the audience will be able to:
Identify (traditional and emerging) applications of learning control
Design learning control laws using state-of-the-art methodologies
Understand the fundamentals and historical development of data-driven learning control
Describe and apply state-of-the-art design methods, including optimization-based approaches, frequency-domain design, norm-optimal discrete repetitive processes, and spatial/distributed learning control.
Connect theoretical concepts to practical applications through case studies and industrial examples.
Identify emerging challenges and research directions in data-driven learning control.
Intended Audience and Desired Prerequisite:
Academics and researchers working in control systems, robotics, mechatronics, and automation.
Postgraduate students interested in data-driven and learning-based control methodologies.
Industrial engineers and practitioners involved in advanced control, precision systems, and autonomous or intelligent systems.
learning-based and repetitive control at major international conferences, including the American Control Conference (ACC), the IEEE Conference on Decision and Control (CDC), and the IFAC World Congress 2017. These sessions have consistently been well attended, typically attracting 35–40 participants, with balanced participation from North America, Europe, and Asia, demonstrating sustained community interest and broad international appeal.
A related workshop was presented at the IFAC World Congress 2017, where the primary emphasis was on recent theoretical developments. In contrast, the proposed IFAC 2026 workshop adopts a distinctly different and more application-oriented focus. While fundamental principles and dominant design frameworks of data driven learning control are introduced, the workshop places particular emphasis on practical implementations, case studies, and real-world deployment challenges.
By foregrounding applications and practical insights, the IFAC 2026 workshop aims to broaden its appeal to both academic researchers and practitioners, and to demonstrate how data-driven learning control methods are being translated into impactful engineering solutions.
Speakers:
Professor Kevin L. Moore, Colorado School of Mines, USA
Professor Bing Chu, University of Southampton, UK
Professor Tom Oomen, Eindhoven University of Technology, The Netherlands
Professor Ying Tan, University of Melbourne, Australia
Professor Kira Barton, University of Michigan, USA
Workshop Schedule (Tentative):
09:00-09:05 Opening –Welcome, workshop overview, and motivation (Ying Tan)
09:05-09:45 Introduction to ILC and Frequency-Domain Design (Tom Oomen)
09:45-10:25 Classical ILC/DRP Design from an Algebraic (Matrix-Fraction) Perspective (Kevin Moore)
10:25-10:40 Morning Coffee Break
10:40-11:20 Optimisation-Based Design (Bing Chu)
11:20-12:00 Learnability and Persistence of Excitation (Ying Tan)
12:00-13:30 Lunch Break
13:30-14:10 Spatial and Distributed Learning Control (Kira Barton)
14:10-14:30 Tom Oomen: Data-Driven Estimation
14:30-14:50 Bing Chu: Data-Driven ILC (continuous) and Norm-Optimal ILC (discrete)
14:50-15:10 Kevin Moore: Data-Driven DRP
15:10-15:40 Afternoon Coffee Break
15:40-16:00 Ying Tan: Fast-convergence ILC
16:00-16:20 Kira Barton: Economic ILC
16:20-17:00 Ying Tan, moderator: Open Discussion and Concluding Remarks Discussion