Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Timm Faulwasser

Lars Grüne

Jonas Schießl

Abstract:

Predictive control is one of the industrially most successful advanced control methods. Driven by different applications, one focal point of ongoing research is the consideration of stochastic uncertainties. In energy systems non-Gaussian uncertainty often arises from modeling the uncertainty surrounding volatile renewables and consumption behavior, while in autonomous systems stochastic models are used to capture system motion. Moreover, data-based modeling introduces stochastic uncertainties into the control loop. Recent research brought significant progress in this area, but at the same time revealed crucial open questions. This workshop will review trends and recent developments and will thus provide a platform fostering discussion between speakers and participants on different career levels.

Website: https://www.tuhh.de/ics/workshops-seminars/ecc2026-stochastic-control

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Cosimo Della Santina

Kaoru Yamamoto

Manuel Keppler

Sylvia Herbert

Abstract:

Robot control has matured into a rich and diverse discipline, yet its intellectual coherence is increasingly strained by fragmentation across paradigms, application domains, and publication venues. Classical problems (stability under interaction, modeling uncertainty, underactuation, hybrid dynamics, etc.) are often treated as “solved” by practitioners, yet they persistently reappear in modern robotic systems operating in contact-rich, uncertain, and learning-enabled environments. At the same time, new challenges and opportunities are emerging, ranging from unconventional robotic platforms (e.g., soft and biohybrid robots) to the growing role of machine learning and large-scale physical data. This workshop provides a focused forum to reassess which problems in robot control remain fundamentally open, how their formulation has evolved with advances in hardware, autonomy, and learning, and which challenges genuinely require new control-theoretic perspectives rather than incremental refinements. The emphasis is on conceptual clarity, modeling assumptions, and the limits of existing methods, rather than polished experimental performance. The workshop brings together invited speakers spanning control traditions, including nonlinear and geometric control, passivity-based and interaction control, hybrid and optimization-based methods, learning-based control, and whole-body robotics. Each speaker will be asked to identify one to three open control challenges they consider critical for robotics moving forward, and to clarify whether these are long-standing unresolved questions or newly emerging ones. By creating explicit space for reflection and open questions, the workshop aims to strengthen the intellectual foundations of robot control and reinforce its role within robotics.

Website: https://ieee-ras-robot-control.github.io/oceo_rc_ecc26/

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Andreas Rauh

Marit Lahme

Abstract:

Cyber-physical systems are characterized by a strong interplay between hardware and software and only obtain their specific properties concerning (energy) efficiency, robustness, fault tolerance and resilience against disturbances and adversarial attacks by state estimation and control approaches which are robust against uncertainty. In recent years, cyber-physical systems have significantly grown in their complexity concerning the number of components. Moreover, their importance increases in many domains of critical infrastructure, for example, in transportation and energy supply. Due to the high safety levels required for these kinds of systems, it is indispensable to guarantee functional properties such as trajectory tracking capabilities, stability despite omnipresent uncertainty and environmental influences, and robustness against an imperfect knowledge of the exact system dynamics. To ensure these properties, set-based representations, i.e., worst-case bounds, for uncertain parameters, disturbances, and the influence of measurement noise can be used to develop estimation and control procedures so that desired performance indicators are met with certainty. Although this is extremely appealing from a methodological and application point of view, the use of set-based approaches is still underrepresented in the literature and in industry. Within this workshop, we aim at presenting fundamentals and most recent developments concerning set-based identification, state and disturbance estimation, fault detection, and control methods. The applicability of the presented methods is highlighted by a collection of real-life use cases, ranging from estimation within the energy domain, over reliable control of thermal systems, the detection of faults and cyber attacks to localization and navigation in maritime robotics.

Website: http://www.uol.de/interval-workshop26

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Nicolas Espitia
Jean Auriol
Lassi Paunonen

Abstract:

The workshop focuses on recent advances in control and estimation of distributed parameter systems (DPS) modeled by PDEs and time-delay systems, which arise in applications such as heat transfer, fluid dynamics, structural vibrations, and battery systems. It addresses the gap between rigorous infinite-dimensional control theory and digital implementation challenges, with emphasis on scalable, real-time, and robust methods. The program is structured around four themes: advanced PDE control (backstepping and port-Hamiltonian approaches), digital implementation and discretization (including MPC for boundary control systems), integration of data-driven and AI-enhanced methods (neural operators, reduced-order modeling), and cross-disciplinary collaboration between PDE/control theory and machine learning.

Website: https://paunonenmath.com/ECC26-DPS-workshop/

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Mayank S. Jha

Bayu Jayawardhana

Abstract:

Safety-critical autonomy increasingly relies on learning-enabled controllers that must operate under uncertainty, limited data, and changing environments. This full-day ECC pre-tutorial workshop surveys recent advancements in safe control design and safe learning, emphasizing methods with provable guarantees and demonstrated performance on real robotic and cyber-physical platforms. The workshop begins with safe reinforcement learning perspectives that embed Control Barrier Function (CBF) invariance constraints into reward shaping and enable safe exploration via input-to-state safety concepts. We then connect optimization-based control and learning, highlighting how data-driven model updates can be integrated within MPC-style safety filters without sacrificing real-time feasibility. A central theme is robustness to uncertainty and nonstationarity: we cover distributionally robust control approaches that hedge against partially known disturbance distributions, and robust conformal prediction techniques that maintain probabilistic safety under interaction-driven and more general distribution shifts beyond the i.i.d. regime. The workshop further addresses scalability and structure through constrained multi-task reinforcement learning using natural policy gradient and actor-critic methods in both centralized and decentralized settings. Complementing these approaches, we discuss indirect and direct data-driven safety certification, including Hamiltonian learning from trajectory data to construct conservative safe sets. Finally, we examine distributed safety guarantees for multirobot systems via distributed control barrier functions for safe formation control, supported by experimental demonstrations.

Website: https://mayanksjha.github.io/ecc_26_workshop_proably_safe_msj_bj.github.io/

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Maryam Kamgarpour

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Valentina Breschi

Simone Formentin

Florian Dörfler

Abstract:

Control theory has long provided a rigorous foundation for analyzing and controlling dynamical systems with provable guarantees. However, the increasing reliance on Machine Learning (ML) for modeling and control design raises fundamental questions on the current role of control theory in an increasingly landscape dominated by Artificial Intelligence (AI). This workshop aims to provide participants with insights into how control theory can meaningfully contribute to this landscape, offering an overview of how it can enhance the explainability of ML models, support developments in optimization, enable safe learning for decision making, and impact emerging applications beyond traditional control domains. The workshop will conclude with a final round table discussion on whether control theory should be seen as a legacy discipline or a foundational pillar for the next generation of intelligent systems.

Website: https://sites.google.com/view/ecc2026-controltheoryyesterday?usp=sharing

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Charles Poussot-Vassal

Pauline Kergus

Abstract:

Simplified model construction and model order reduction is essential for making complex systems tractable for control design, optimization, and (uncertain) simulation, yet many real-world systems remain too large or unwieldy for standard approaches. In this context, the Loewner Framework (LF) offers a data-driven, scalable solution allowing constructing accurate surrogate models directly from measured data using only basic linear algebra (e.g., SVD), without iterations or optimization. This makes it uniquely suited for very large, infinite and data-driven systems where traditional methods fail.

Originally developed for linear time-invariant (LTI) systems, the LF has since been extended to nonlinear systems, bridging approximation theory and dynamical systems through rational interpolation and minimal realizations. Its versatility is demonstrated across academic and industrial applications, from aerospace to energy systems.

This workshop is destined to researchers, PhD candidates and engineers working with (i) high-order systems or (ii) frequency-domain input-output data, who need simplified models for control, optimization, analysis or simulation. It will equip participants with:
>> A theoretical foundation in the Loewner Framework for both linear and nonlinear time invariant systems;
>> Hands-on implementation skills using open-source MATLAB tools;
>> Practical experience reducing complex systems from benchmarks, or their own data, during interactive sessions.

Website: https://cpoussot.github.io/ecc_workshop_Loewner.html

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Andrea Carron

Danilo Saccani

Abstract:

Multi-agent systems are rapidly evolving from theoretical constructs to large-scale applications in robotics, logistics, and cyber-physical infrastructures. Examples such as warehouse automation, drone swarms, and smart grids show the need for control architectures that address safety constraints, network limitations, and real-time computation.
While Model Predictive Control (MPC) provides a principled framework for constrained optimization, centralized approaches become computationally intractable and fragile as the number of agents grows. Distributed MPC (DMPC) addresses this by decomposing the global problem into local subproblems coordinated via communication; however, bridging the gap between theoretical stability guarantees and practical, real-time implementation remains a significant challenge.
This workshop offers a comprehensive view of the state-of-the-art in DMPC, structuring the landscape into three complementary modules:
(i) Foundations of Distributed Optimization and Estimation: moving beyond classical consensus schemes to explore sensitivity-based decomposition and the often-overlooked challenge of Distributed Moving Horizon Estimation;
(ii) Learning-Accelerated DMPC: examining how data-driven warm-starting and learning-based policies can drastically reduce computational overhead while maintaining verifiable safety certificates; and
(iii) Contracts and Coordination Primitives: showing how global constraints, such as collision avoidance and connectivity, can be enforced locally via set-based contract abstractions or offline contingency rules when communication is unreliable.
By combining methodological rigor with implementation-oriented insights, the workshop aims to define the roadmap for the next generation of scalable, safe, and efficient multi-agent control systems.

Website: https://sites.google.com/view/dmpc-workshop-ecc-2026

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Giuseppe Belgioioso

Luca Furieri

Andrea Iannelli

Abstract:

Modern algorithms for learning, optimization, and control increasingly operate in dynamic environments, interacting with physical systems, data streams, and other algorithms. This challenges the traditional view of algorithms as isolated computational procedures and motivates interpreting them instead as dynamical systems evolving in feedback with their environment.

This workshop explores how tools from systems theory and automatic control can be used to analyze and design such algorithms. The discussion will focus on two tightly connected themes:
(i) the analysis and design of optimization and learning algorithms from a dynamical systems perspective, and
(ii) real-time algorithms operating in feedback with dynamical systems.

Building on the success of the first edition of this workshop (the largest workshop at ECC 2025), this year’s program brings together a new set of experts from leading institutions, including MIT, ETH Zurich, Cambridge, Boston University, KTH Royal Institute of Technology, TU Eindhoven, and others.

Website: https://sites.google.com/view/sta-ecc-2026-workshop/home

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Thomas Lew

Riccardo Bonalli

Nicolas Lanzetti

Abstract:

Control systems are increasingly deployed in high-uncertainty, high-stakes applications such as autonomous vehicles, robotics, and energy systems. As a result, uncertainty-aware control algorithms have gained significant interest, as they explicitly account for disturbances, model mismatch, and varying environments, enabling principled trade-offs between robustness and performance while ensuring reliability and efficiency.

Motivated by the complexity and scale of modern applications, recent research has focused on extending these methods to increasingly challenging settings, including nonlinear and high-dimensional systems and risk-averse as well as distributionally robust problems, and on developing computationally efficient algorithms capable of real-time operation. At the same time, advances in learning-based control raise new questions about how uncertainty should be modeled, propagated, and exploited in safety-critical control systems.

This workshop brings together researchers working on the theory, algorithms, and applications of uncertainty-aware control to discuss recent advances, current limitations, open challenges, and promising directions for future research.

Website: https://uac-ecc26.github.io/schedule/

Program:

Date & Time:

Tuesday, July 7th
8:30-18:00

Location:

To be announced

Organizers:

Giulia Giordano

Rami Katz

Daniele Proverbio

Abstract:

This workshop discusses methodologies, rooted in control theory and dynamical systems, focused on the analysis and characterisation of robustness and resilience in complex dynamical systems and networks, with emphasis on natural systems (biological, neural, ecological, epidemiological, biomedical and socio-technical systems). Robustness and resilience provide complementary lenses to study the ability of systems to preserve qualitative functions and properties in the presence of uncertainty, disturbances, and perturbations. In parallel, a rapidly growing body of work has investigated early warning signals, both model-based and data-driven, to detect and predict losses of robustness or resilience, or degradation of performance. Despite significant progress, the conceptual unification of these notions, their precise mathematical characterisation in networked systems, and their operational use for monitoring, intervention, control and optimisation remain open and timely challenges. To investigate such challenges, the workshop encompasses three tightly connected themes:
1. Robustness analysis of dynamical networks: theoretical frameworks, metrics, and computational tools, with applications across the life sciences;
2. Resilience in dynamical networks: theoretical frameworks, metrics, and computational tools, bridging insights from natural systems to engineered control design and back;
3. Early warning signals for loss of robustness or resilience: classical and emerging approaches, indicators of critical transitions, data-driven methods, applications, implications for control and decision-making.

Website: https://giordanogiulia.altervista.org/robustness-resilience-earlywarningsignals-workshop/

Program:

Date & Time:

Tuesday, July 7th
9:00-17:00

Location:

To be announced

Organizers:

Andrès Marcos

Marco Lovera

Abstract:

Fault detection & identification (FDI) and parameter estimation methods are becoming increasingly necessary in the aerospace world as the need for autonomy, reliability and performance increases. For example, up until recently launchers did not rely on FDI due to a lack of redundancy, nor on parameter estimation as they were not recoverable systems. But as reusable launchers are becoming the norm, it has become clear that the capability of a company or country to perform during flight accurate estimation of faults and system parameters will determine their competitive edge as this capability directly reduces the downtime of the recovered engines and first-stages. Similarly, helicopters and drones, which are very complex dynamical systems with seldom adequate mathematical models, can achieve better performance and reliability if accurate modeling and estimation of critical parameters can be performed. This full-day course provides a graduate/professional-level introduction to the domains of fault FDI and parameter estimation for aerospace systems. A cursory theoretical presentation for each of these domains is given first presenting their motivation, main concepts and tools –supported by simple examples (with code). This is followed by a detailed presentation of industrial application cases covering from launcher engines to aircraft, helicopters and drones.

Program:

Date & Time:

Tuesday, July 7th
1:30-5pm

Location:

To be announced

Organizers:

Huazhen Fang

Abstract:

The world is on the cusp of a new era of electrification across different sectors of industry and economy. A key technology driving this transformation is lithium-ion batteries. As the best available power source, lithium-ion batteries provide high energy/power density and long cycle life. As they find every-growing use in electric vehicles, electric aircraft, grid storage and autonomous platforms, demands for their performance and safety have been rising. Systems and control theory can play a key role in meeting the needs to advance the application of battery systems, resulting in provable progresses.

This tutorial-style workshop is designed to provide a deep, structured introduction to the state of the art and new frontiers in battery modeling, monitoring and control, with a focus on integrating physical insights and control-theoretic rigor with practical implementation. The workshop will be particularly relevant to researchers and practitioners within the systems and control community looking to expand their research towards developing and applying control-theoretic methods for lithium-ion batteries and emerging battery-powered systems. ​Participants will leave with a structured understanding of how to model, analyze, and control batteries across a range of industrial electronics applications. They will gain exposure to both theoretical tools and practical design insights necessary for developing the next generation of battery management systems. This tutorial also aims to serve as a bridge between foundational research and real-world applications, inspiring future research directions and exploration.

Webpage: https://www.issl.space/ecc2026-bms-workshop.html

Program:

Date & Time:

Tuesday, July 7th
9:00-12:30

Location:

To be announced

Organizers:

Damiano Varagnolo

Abstract:

Modern educational environments increasingly generate digital traces of learning through platforms for continuous formative assessment, such as micro-quizzes, online exercises, and interactive learning environments. These tools allow instructors to collect data about how students’ knowledge evolves during a course. Such signals can be interpreted as measurements of a dynamical system and analyzed using modeling, estimation, and feedback concepts familiar to the control community.

This workshop introduces participants to the idea of treating the classroom as a dynamical system whose internal state represents student knowledge and engagement. Participants will learn how to design and use platforms for continuous formative assessment to collect classroom data, how to preprocess and analyze these signals, and how to estimate simple models of learning dynamics using system identification techniques. The workshop will also discuss more advanced representations, including state-space models and knowledge-graph approaches.

The workshop combines short lectures with demonstrations and hands-on exploration using example datasets and open-source tools. Its goal is to expose control researchers and educators to a new application domain where modeling, estimation, and feedback ideas can support evidence-based teaching and learning.

Website: https://github.com/damianovar/ecc2026-workshop-learning-dynamics

Program:

Date & Time:

Tuesday, July 7th
13:30-17:00

Location:

To be announced

Organizers:

Hozefa Jesawada

Abdalla Swikir

Fares J. Abu-Dakka

Giovanni Russo

Abstract:

This workshop explores the application of the Free Energy Principle (FEP) and active inference to control, emphasizing how a control-theoretic view of FEP connects neural dynamics with robotic embodiments. FEP models natural agents as minimizing variational free energy—a bound on surprise—under a generative model of the world, thereby unifying perception, action, and learning. The workshop first presents theoretical foundations and a density-control interpretation of FEP, then examines neural mechanisms for sensing and policy computation necessary to instantiate FEP in closed-loop systems such as robots. Concrete examples focus on robotic manipulators, highlighting how FEP-based controllers can handle uncertainty, enable compliant and safe interaction, and learn from demonstrations, with topics including variational inference for state estimation, active inference for motion planning, distributionally robust formulations, and integration with impedance control. Participants will gain both conceptual understanding and practical insights for implementing FEP-based controllers on real robotic systems.

Website: https://jamalihuzaifa9.github.io/FEP-Workshop-ECC-2026/

Program: