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:

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: 

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: 

Intended Audience and Desired Prerequisite:

In recent years, the workshop organizers have been actively involved in organizing invited sessions related to 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:

Workshop Schedule:

Speaker: Tom Oomen, Eindhoven University of Technology, The Netherlands

Description: Iterative Learning Control (ILC) and Repetitive Control (RC) can compensate for repeating disturbances. The aim of this part is to develop a basic ILC algorithm. A frequency domain viewpoint is taken for the analysis and design. Examples show how such an approach is particularly suitable for applications where frequency domain system models are used. Several implementation aspects, including noncausal designs, are highlighted. The focus is on ILC design, and it is pointed out how it relates to a very similar RC framework. 

Speaker: Kevin L. Moore, Colorado School of Mines, Golden CO US

Description: In this two-part talk we introduce and summarize the classical results for controller design when systems operate in an iterative, or repetitive, manner, with both a time-domain axis (along-the-pass) and on an iteration-domain axis (pass-to-pass). In Part 1, focusing on discrete-time systems, we introduce a one-step iteration delay operator, analogous to the classical one-step time delay operator. With this concept we define an algebraic, matrix-fraction framework from which we can simultaneously design controllers that operate both along-the-pass and from pass-to-pass. 

Speaker: Kevin L. Moore, Colorado School of Mines, Golden CO US

Description: In Part 2 of this two-part talk, we illustrate how the algebraic, matrix-fraction framework introduced in Part 1 enables a variety of classical time-domain control concepts, such as the internal model principle, MPC, Kalman filtering, PID design, monotonic convergence, l1, H2, H∞, and more, to be applied in the iteration-domain. We discuss several of these ideas in detail with examples, including PID design with current iteration tracking error (CITE) for monotonic convergence and internal model principle-based design.

Speaker: Tom Oomen, Eindhoven University of Technology, The Netherlands

Description: An optimization-based approach to designing ILC controllers is presented. This allows a systematic design approach, and initially an approach based on linear algebra is developed. This allows to systematically analyse different notions of convergence, including monotonicity, as well as time-varying solutions. Design considerations are outlined, as well as relations to Riccati-based solutions.  

Speaker: Ying Tan, University of Melbourne, Australia

Description: In this talk, we introduce the concepts of learnability and persistence of excitation (PE) as fundamental principles underlying iterative learning, adaptive control, and human motor learning. We show how persistence of excitation governs convergence speed, parameter identification, and learning performance, and discuss why good tracking does not necessarily imply successful learning. Using examples from iterative learning control and human motor learning, we illustrate how feedback, feedforward adaptation, and excitation interact during the learning process. The resulting framework provides practical insights into designing learning systems that achieve faster convergence, improved robustness, and enhanced learning efficiency.

Speaker: Leontine Aarnoudse, Norwegian University of Science and Technology

Description: Including a nonlinearity in a controller can increase the design freedom significantly. In this talk, we show how this idea can be applied to norm-optimal and frequency-domain ILC, develop convergence conditions that allow for intuitive tuning, and use the additional design freedom to differentiate between repeating and non-repeating disturbances. This leads to fast and accurate attenuation of repeating disturbances, without amplifying iteration-varying disturbances and without sacrificing the convergence speed.

Speaker: Ying Tan, University of Melbourne, Australia

Description: In this talk, we introduce fast-convergence iterative learning control (ILC) algorithms based on modified rescaled gradient descent. We show how optimisation techniques can be incorporated into the ILC framework to significantly accelerate convergence while maintaining robustness under practical engineering conditions. Unlike existing rescaled gradient methods that rely on restrictive assumptions, the proposed approach relaxes these requirements and provides practical design procedures for implementation. We discuss the trade-off between convergence speed and learning accuracy, present convergence properties, and illustrate the effectiveness of the proposed algorithms through representative examples and applications.

Speaker: Kevin L. Moore, Colorado School of Mines, Golden CO US

Description: Recently, Willem’s Fundamental Lemma has been used to develop what can be considered to be model-free, data-driven control strategies. In this talk we show how to apply this idea to ILC and DRP controller design, including for norm-optimal ILC, thus producing truly model-free ILC/DRP algorithms.

Speaker: Tom Oomen, Eindhoven University of Technology, The Netherlands

Description: Traditional ILC uses model-based designs to learn optimal command signals that minimize the error signal. Here, we will outline data-driven techniques to learn both command signal and other system properties, including system norms.