Understanding and interpreting complex coupled systems remains one of the biggest challenges in the world. Examples of these applications range from industrial manufacturing to the temporal characteristics of real-world conditions. To address this challenge, this paper presents a novel approach to structured program code development based on graph generation. The problem is considered from the perspective of an inductive link prediction problem structured by an evolutionary algorithm. The self-adaptation of relevant knowledge takes place in a closed loop, where systematic relationships are constantly improved and extended. The required system behaviour is mapped step by step, taking into account constraints, limitations and expert knowledge. Structured graph generation is used to represent logic functions and interpret complex coupled relationships. The presented strategy enables targeted plant control through customised program code concepts, which are used to optimise processes and increase efficiency. The approach is validated through the design of interpretable programmable control logic on an industrial manufacturing process and obtain a comparable solution to the work of a trained professional. The achieved results demonstrate the next level of independent self-optimisation in learning and interpreting logical relationships in automation.

In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recognised for their efficacy in enabling self-optimizing distributed multi-agent systems and offer a proven convergence guarantee, which facilitates collaborative player efforts towards global objectives. Our study strives to replace conventional ad-hoc random exploration-based learning in SbPGs with contemporary gradient-based approaches, which aim for faster convergence and smoother exploration dynamics, thereby shortening training duration while upholding the efficacy of SbPGs. Moreover, we propose three distinct variants for estimating the objective function of gradient-based learning, each developed to suit the unique characteristics of the systems under consideration. To validate our methodology, we apply it to a laboratory testbed, namely Bulk Good Laboratory Plant, which represents a smart and flexible distributed multi-agent production system. The incorporation of gradient-based learning in SbPGs reduces training times and achieves more optimal policies than its baseline.

The integration of advanced robotic technologies with modern software platforms has enabled significant advancements in industrial automation. This paper aims to introduce a novel workflow to integrate ABB industrial robot manipulators and the Robot Operating System (ROS), where communication is built through OPC Unified Architecture or a virtual controller with EGM+RWS. Moreover, we also provide a method to deploy the ROS environment using container technology in our workflow. The developed workflow was tested and evaluated in a real-world welding application, demonstrating its capability to automate the welding process of steel looped hooks for supermarket shelves with high accuracy and efficiency, in which the path planner for the robot is obtained from MoveIt by ROS. The introduced workflow can also be integrated with the MLPro framework, which enriches the workflow with machine learning-related algorithms, e.g. for path planning. The results of this study demonstrate the potential of integrating ABB industrial robot manipulators with ROS for industrial process automation and highlight the benefits of using ROS as a software platform for industrial robotics applications.

The digital transformation of automation places new demands on data acquisition and processing in industrial processes. Logical relationships between acquired data and cyclic process sequences must be correctly interpreted and evaluated. To solve this problem, a novel approach based on evolutionary algorithms is proposed to self optimise the system logic of complex processes. Based on the genetic results, a programme code for the system implementation is derived by decoding the solution. This is achieved by a flexible system structure with an upstream, intermediate and downstream unit. In the intermediate unit, a directed learning process interacts with a system replica and an evaluation function in a closed loop. The code generation strategy is represented by redundancy and priority, sequencing and performance derivation. The presented approach is evaluated on an industrial liquid station process subject to a multi-objective optimisation problem.