Working groups
Dynamic System Simulation in the Lifecycle of Automation Systems
Responsible: Prof. Georg Frey (georg.frey(at)aut.uni-saarland.de)
The TC 3.1 working group “Dynamic System Simulation in the Lifecycle of Automation Systems” focuses on the use of forefront modeling and simulation methods that can be used to optimize engineering processes as well as the operating phase. The availability of modelling languages, exchange formats and scalable computational resources opens up more possibilities for the use of simulations. These range from simulation models used in the design phase of control systems to simulation models directly used in real-time control as soft sensors or for optimal planning in MPC-like approaches. Complex automation systems are built from components often designed and manufactured by different parties. The interchange and re-use of simulation models describing these components can greatly lower system development time and cost. The inclusion of dynamic simulation models in digital twins could be seen as an upcoming option here. The development of modelling languages as Modelica and exchange formats like FMU/FMI however is not primarily driven by the control and automation community.
- The aim of this WG is to foster joint discussion and research on modelling languages for dynamic simulation models, description tools, exchange mechanisms, execution models, and use cases of simulation in the automation domain with its specific requirements (real-time, dependability, security, IP protection).
The working group aims to address researchers (young and senior) with a focus on model-based engineering and industrial practitioners from modeling & simulation in machine and plant manufacturing and further industrial domains.
IT/OT-Security in Automation Systems
Responsible: Marwin Madsen (marwin.madsen@kit.edu)
We face a tremendous rise of cyber security attacks on production systems (automation systems) and at the same time there is a lack of IT/OT-security methods and measures in automation systems. The trend towards decentralized, highly interconnected and modular automation systems requires new advanced IT-Security processes and methods.
he TC 3.1 working group “IT/OT-Security” focuses on the discussion of methods, tools, best practices and use-cases for a stable and constantly evolving IT/OT-Security. Topics are:
- Development of new IT/OT-Security Methods for automation systems
- Discussion of best practices on an international level
- White-Papers and Workshops on IT/OT-Security
The working group aims to address researchers (young and senior) with a focus on IT/OT-Security and industrial practitioners from Industrial Security for process industries, plant manufacturing and further industrial domains.
Education in Automation Technologies
Responsible: Prof. Alois Zoitl (alois.zoitl(at)jku.at)
Computers in control are getting more important and diverse. We constantly face more topics and wider areas to teach. This requires dedicated teaching environments, e.g.
- Physical testbeds and/or
- Virtual testbeds
The TC 3.1 working group “Educatiuon in Automation Technologies” focuses on the exchange on best teaching practices, the sharing of teaching materials as well as the derivation of a knowledge and teaching map. It aims to address researchers (young and senior) with several education experience in the field of automation technologies.
Information Models in control engineering
Responsible: Prof. Dr. Mike Barth (mike.barth(at)kit.edu)
In various use-cases in industry and research, a common concept for semantic information modeling is needed in order to implement these use-cases. This especially holds for heterogeneous, distributed systems, such as Cyber-Physical Production Systems (CPPS), but similarly in other domains. These distributed systems have to deal with individual sub-systems which stem from different suppliers but have to exchange information to implement the overall system function. Not only the communication means have to be agreed upon, but further the content of the exchanged information must be understood correctly by all components, which requires that the information is based on a semantic description.
This holds for all phases of the life cycle of an automated system: The first use-case for semantic modeling can be found in the early life-cycle phase of a system, i.e. the engineering of the system. The engineering of automated systems usually relies on several engineers who are working on different tasks that have to be accomplished through the engineering phase. Especially for complex systems, model-based engineering as well as the systematic reuse of components can provide relevant efficiency increases. However, the information created in the engineering phase has to use a common semantic, not only for avoiding inefficiencies or faults throughout the engineering process, but also to support the cooperation between engineering teams.
Concerning the modeling of skills for the purpose of reconfiguration of manufacturing systems, there has been remarkable research effort on information modeling already in order to model skills for reconfiguration and orchestration purposes.
Concerning the orchestration of manufacturing services by means of service oriented architectures (SOA), semantic modeling of services is necessary in order to provide a common understanding of the post- and preconditions of a service, and semantic descriptions of manufacturing services have been proposed in order to provide information for an agent based orchestration system. Furthermore, semantic descriptions of manufacturing systems can be used to schedule the resources, e.g. workers and machines, of a factory.
Furthermore, semantic modeling of systems is becoming relevant for diagnosis and maintenance systems as well: semantic descriptions about the condition of systems are required to provide a common information model for the diagnosis of distributed manufacturing systems, i.e. semantic descriptions of the systems that are monitored and the maintenance knowledge.
Desipte these needs, there are still many open research questions regarding the creation and use of semantic enrichment of information in the design, implementation, operation and optimization of automated systems. The aim of this sub-WG is to foster joint discussion and research on these topics.
AI Agents and Multi-Agent Systems in Industrial Applications
Responsible: Dr.-Ing. Felix Gehlhoff (felix.gehlhoff(at)hsu.hamburg) & Prof. Dr. Christoph Legat (christoph.legat(at)tha.de)
We bring together international research and industrial application of agent-based systems in industrial automation domains such as production systems, logistics, agriculture, robotics, and energy infrastructures. In increasingly interconnected environments – ranging from embedded devices and cyber-physical systems to enterprise-level IT integrated via the Internet – the group examines how distributed, intelligent, and autonomous entities can improve coordination, adaptability, and resilience. Its focus includes the systematic exploration and advancement of architectural approaches, communication and coordination, autonomous and human-on-the-loop / human-in-the loop decision making in all more and more interconnected and merged life-cycle phases from engineering and operation to maintenance and circularity; taking into account established works in (applied) Artificial Intelligence on agent and multi-agent systems as well as emerging LLM-, foundation-model-, and multimodal-agent-based approaches, including a broad set of possible technological realizations, such as robotics or agentic AI workflows. The group addresses possibilities of classical and modern agent technologies, while also considering key challenges such as safety, reliability, reproducibility, latency, trustworthiness, transparency and integration into CPS, PLC, and MES environments, benchmarking, and standardization.
The working group serves as an international platform to connect national initiatives with global research and application landscape. Its aim is to foster exchange and transfer within and between academia and industry across countries and communities by, among others, initiating joint publications, books, organizing co-located events like special sessions, invited sessions, and workshops. Researchers, practitioners, and experts from industry and academia are warmly invited to contribute their perspectives and help shape the future of agent-based intelligent systems. Ultimately, the working group serves as an open platform for exchanging research results, industrial experiences, and forward-looking ideas. By tightly coupling recent advances in agent-based and agentic AI research with hands-on industrial insights, it enables a continuous feedback loop between theory and practice, accelerating the realization of intelligent, decentralized, and adaptive industrial systems.
Interested in joining the working group or get further information? Just send an e-mail: join(at)industrial-agents.org