This paper proposes a novel AI agent for reasoning, planning, and testing industrial automation applications. The AI agent accepts operator instructions in natural language and performs semantic reasoning over functional requirements to generate an optimized cost-effective action plan. This plan comprises a sequence of executable actions that can be deployed in industrial control systems (ICS) to enable efficient and sustainable machine operation. Then the AI agent automates the testing of control systems by comparing the agent’s planned and executed actions against expected outputs, ensuring requirement conformance and enhancing system reliability. Experimental results demonstrate the effectiveness of the AI agent in generating sustainable operational plans and validating control system behavior for laboratory scale case studies, underscoring its potential in the future of intelligent industrial automation.

This article proposes a method for the automatic generation of a plant model and monitoring using process mining algorithms based on recorded event logs. The behavioral traces of the system are captured by recording event logs during plant operation in either manual control mode or with an automatic controller. Process discovery algorithms are then applied to extract the logic of the process behavior properties from the recorded event logs. The result is represented as a Petri net, which is used to construct the state machine of the plant model and monitor and is in accordance with the IEC 61499 Standard. The monitor is implemented as a function block and can be deployed in real time to trigger an error signal whenever there is a deviation from the actual process scenario. The plant model and controller are connected in a closed loop and are used for the formal verification of the system with the help of the “fb2smv” converter and symbolic model checking tool NuSMV.

This paper presents a novel approach for tracking products and processes within industrial multi-agent control systems by leveraging blockchain technology. The suggested solution facilitates the recording and validation of every step involved in creating a tailored product through the utilization of Ethereum-based smart contracts. The OWL ontology is used to describe agents and their capabilities and these software agents interact with IEC 61499 function blocks for process execution. The software agents record process events at each stage on the blockchain and the latter smart contract helps to trace and verify these process sequences of the customised product.

This paper introduces an innovative multiactor framework that harnesses the potential of LLMs to augment the functionalities of ICS. By integrating conversational AI technologies, this framework significantly improves human-machine interactions, enabling sophisticated analysis and visualization of intricate data sets. The core of the system comprises specialized LLM actors that interact through a LangGraph-based multiactor framework, addressing various aspects of IEC 61499 control systems including PLC code analysis, SQL query execution, and data visualization. This integration enables operators to interact with the control system using natural language, significantly reducing technical barriers and enhancing the accessibility and usability of complex industrial systems.

A learning management system (LMS) is a web-based technology developed to improve the learning process in educational institutions. This study addresses the drawbacks of existing LMS by developing DeLMS (Decentralized Learning Management System) and aims to revolutionize the way educational institutions manage and deliver educational content using Ethereum smart contracts and the IPFS (InterPlanetary File System). By leveraging Smart contracts and IPFS cutting-edge technologies, DeLMS provides a secure, transparent and decentralized platform for managing and delivering educational content. The content addressing feature of IPFS eliminates the need for constant requests to centralized servers for the location of files, resulting in faster file transfer and reduced energy consumption. The proposed system has the potential to change the way educational institutions operate, making it more accessible and beneficial for both students and educators.

This paper summarises recent developments in the application of modular formalisms to model-based verification of industrial automation systems. The paper is a tribute to the legacy of Professor Hans-Michael Hanisch who invented Net Condition/Event Systems (NCES) and passionately promoted the closed-loop modelling approach to modelling and analysis of automation systems. The paper surveys the related works and highlights the impact NCES has made on the current progress of modular automation systems verification.

This paper presents a swarm-based architecture for the composition of mechatronic systems from smart components complemented by a Virtual Commissioning (VC) environment. The architecture is based on the IEC 61499 standard. The proposed solution enables plug-and-play composition of the system which is ready to operate ”out of the box” right after it was composed without extra programming.

In this paper, we describe a method to automatically derive the controller for an automated process by an interactive learning approach using a simulation model developed in Visual Components 3D simulation software. The latter is used to record the events of the processes and the controller is generated as an IEC 61499 function block. To create different process scenarios, the actuator signals are triggered manually in appropriate order. The controller logic in Petri net is derived by process discovery algorithms with help of recorded events and conversion of Petri net to IEC 61499 function blocks is done by a software tool configured with a set of transformation rules.