Workshops
We are excited to announce a series of engaging workshops at DYCOPS 2025. These workshops, led by researchers and industry experts, will provide participants with hands-on experiences and practical insights into the latest developments in the field of dynamic systems and control.
Workshops will cover a variety of themes, offering valuable opportunities to enhance your skills, network with peers, and gain knowledge applicable to your field.
Workshops will take place as a pre-symposium program on June 16, 2025, at the DYCOPS 2025 venue, the Radisson Blu Carlton Hotel. There will be five workshop sessions, including four full-day workshops and two half-day workshops. Since all full-day workshops will run simultaneously, participants may register for either one full-day workshop, one half-day workshop, or a combination of two half-day workshops.
The registration fees for the workshops are 120 EUR. These fees include coffee breaks.
Registration for the workshops is available as part of the standard registration form for both regular and student participants, as well as a separate option for workshop-only attendees. Registration for workshops is now open and available via registration page.
Optimal operation and advanced control using a decomposed architecture with simple elements
Presenters: Sigurd Skogestad (NTNU, Norway), Krister Forsman (Perstorp Co., Sweden)
How can you control a complex plant effectively using simple elements with a minimal amount of modelling? How can you put optimization into the control layer? Industry has been using simple and effective as “advanced regulatory control” schemes for almost 100 years. The objective of the workshop is to provide a systematic approach for designing such control systems. The approach is illustrated on numerous real industrial applications. The target audience includes both practicing control engineers as well as PhD students and teachers from academia.
Real-time Data to Real-time Decisions: A Tutorial Workshop on Data-driven Real-time Optimization for Process Operations
Presenters: Dinesh Krishnamoorthy (Eindhoven University of Technology, Netherlands), Mehmet Mercangöz (Imperial College London, UK), Marta Zagórowska (TU Delft, Netherlands), Lucas Bernardino (SINTEF Energy, Norway), Valentina Breschi (TU Eindhoven, Netherlands)
Real-time optimization (RTO) is crucial for modern industrial process operations, enabling quick adaptation to dynamic conditions and delivering substantial economic and operational benefits. However, implementing RTO faces significant challenges, such as the high cost of developing accurate models and the risk of model mismatch in complex, dynamic systems. Traditional model-based approaches often struggle to adapt effectively, leading to suboptimal or unsafe decisions.
This workshop provides an in-depth tutorial on data-driven RTO, which leverages real-time data to overcome the limitations of model-based optimization. Targeted at PhD students, researchers, and practitioners, it explores how data-driven methods reduce reliance on high-fidelity models, addressing modeling costs and mismatches to facilitate effective decision-making in complex systems.
The program includes an introduction to data-driven RTO, a detailed exploration of constrained Bayesian Optimization (BO) for real-time control, and methods to manage constraints without probabilistic models (D. Krishnamoorthy). It also covers feedback-based approaches to optimize partially unknown systems and introduces self-learning capabilities for industrial plants (M. Zagorowska). Topics include scalability, safety-critical constraints, and incorporating domain knowledge through advanced frameworks such as ensemble uncertainty estimation and bilevel programming (M. Mercangoz). The workshop will also include how meta-learning leverages prior experience to enhance real-time learning and adaptation across optimization, modeling, and control tasks (V. Breschi). Additionally, the workshop addresses measurement-based gradient estimation (L. F. Bernardinho), feedback optimization for interconnected systems, and multi-agent frameworks for distributed optimization (D. Krishnamoorthy). Participants will gain insights into cutting-edge techniques for adaptive, scalable, and decentralized process optimization, equipping them with practical knowledge for real-world applications.
Nonlinear model predictive control for anything: Designing fast, efficient, and safe neural network controllers
Presenters: Adamek Joshua (TU Dortmund, Germany), Joel Paulson (The Ohio State University, USA), Ali Mesbah (UC Berkeley, USA), Sergio Lucia (TU Dortmund, Germany)
Neural networks are a very promising tool to implement complex control algorithms. They are extremely fast to evaluate, rendering real-time control of challenging applications possible. Their deployment on embedded hardware is simple, due the user-defined memory size, which allows to adjust the architecture on the memory requirements, as well as the simple evaluation of the neural network controllers, which just involves nonlinear and linear function evaluations, that can be implemented on almost any hardware. Furthermore, almost any arbitrary control algorithm on any scale can be represented by a neural network because of their general expressiveness. However, finding a suitable parametrization is very challenging and existing methods such as reinforcement learning struggle due to data inefficiencies and limited use of knowledge about the control task. To alleviate these challenges, it is possible to exploit synergies between nonlinear model predictive control (NMPC) and neural networks that try to imitate the NMPC behavior. This approach provides a very promising baseline for neural network control, as it includes physical knowledge about the controlled system and allows for application of tools from control theory and optimization to achieve optimal performance under consideration of constraints and uncertainties.
The objective of this workshop is to provide a hands-on overview from neural network controller design to deployment on real-world applications using robust NMPC as a baseline to be imitated. To this end, this workshop will first give an overview of the large body of current research on neural network-based control based on nonlinear MPC. A short hands-on introduction to robust NMPC and its implementation using open-source tools will be presented. A tutorial on supervised and unsupervised imitation learning approaches to synthesize the neural network controller from the robust NMPC baseline will be covered, also including practical considerations of efficient data-sampling strategies and an introduction on probabilistic safety and performance guarantees. Finally, this workshop will showcase the deployment on embedded hardware and application to real-world problems.
Data Science Practitioner Experience and Industrial Tips and Tricks of the Trade
Presenters: Leo Chiang, Birgit Braun, Ricardo Rendall (The Dow Chemical Company, USA), Marco P. Seabra dos Reis (University of Coimbra, Portugal)
This full day workshop aims to introduce attendees on the portfolio of data science methods and share real-world experience in developing and implementing data science models to solve industry-relevant problems. Specifically, we will dive into practical data science workflow from visualization, data preprocessing, model development to implementation and then summarize best practices on how to frame business opportunity as analytics problem to deliver value.
Presenter: Daniel Y. Abramovitch (Agilent Technologies, USA)
Computation is an essential component of implementing any real-world control system, but the details of how to make this work are often either left to the individual contributors to figure out or handed off to turn key vendors. This workshop intends to provide insights, methods, and concrete examples into three major pieces of this subject. First, the workshop will present recent tutorial material (ACC 2023) from the author on real-time computing issues for control systems. This material explains the principal factors affecting the four computing chains inside a feedback system. After this overview, the workshop will spend time on an often-neglected area of computation for control system measurements, whether they be used in the control loop operation or in the system identification used in model building for control. Finally, the workshop will hone in on specific programming methods and components in the controller itself, describing efficient implementation methods and structures. Together these three thrusts should equip the participant with tools that they can apply almost immediately in their work.
For more details see extended description.
Presenter: Daniel Y. Abramovitch (Agilent Technologies, USA)
The proverbial “gap” between control theory and practice has been discussed since the 1960s, but it shows no signs of being any smaller today than it was back then. Despite this, the growing ubiquity of powerful and inexpensive computation platforms, of sensors, actuators and small devices, the “Internet of Things’, of automated vehicles and quadcopter drones, means that there is an exploding application of control in the world. Any material that allows controls researchers to more readily apply their work and/or allows practitioners to improve their devices through best practices consistent with well understood theory, should be a good contribution to both the controls community and the users of control. This workshop is intended as a small but useful step in that direction.
For more details see extended description.