Volume 72, Issue 4

This paper introduces a new diagnostic approach for concurrent discrete event systems (DES) in automated production plants. The proposed diagnostic procedure is based on Petri net models of the DES. Discrepancies between the Petri net models and event sequences, which are observed during the operation of the production plant, are detected as potential faults. A case-based diagnosis is then carried out to identify fault types that are easy to interpret for humans. The proposed diagnostic procedure is evaluated using an industrial conveying system.

There is a class of systems that can only be stabilized by unstable compensators. However, for most practical systems suitable for use with linear regulators, this is possible with stable ones. To avoid exponentially unstable results in the design of pole placing PI-compensators it is of crucial importance to consider the position of the transmission zeros. This also has a favourable effect on the disturbance rejection. In multi-input multi-output (MIMO) systems, however, not only the zero position but also the zero direction is of importance. In this paper, a design method for multivariable PI-controllers in the time- and in the frequency-domains is presented which helps to avoid the occurrence of exponentially unstable controllers. A modified design method for multivariable PI-controllers, recently published in this journal, facilitates the procedure.

Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method for automated tuning, applying it to large and high-dimensional search spaces remains challenging. We extend a recently proposed local variant of BO to include crash constraints, where the controller can only be successfully evaluated in an a-priori unknown feasible region. We demonstrate the efficiency of the proposed method through simulations and hardware experiments. Our findings showcase the potential of local BO to enhance controller performance and reduce the time and resources necessary for tuning.

In the joint research project PRORETA 5, building blocks for automated driving in urban areas have been developed, implemented, and tested. The developed blocks involve an object tracking for cars, bicycles, and pedestrians that feeds a multimodal object prediction which is able to predict the traffic participants’ most likely trajectories. Then, an anytime tree-based planning algorithm calculates the vehicle’s desired path. Finally, logic-based safety functions ensure a collision-free trajectory for the ego vehicle. The mentioned building blocks were integrated and tested in a prototype vehicle in urban scenarios. Furthermore, a novel general framework for specifying and testing traffic rule compliance has been developed. In this paper, the automated driving concept of PRORETA 5 is introduced and the developed methods are briefly explained.

Flexible production processes and applications for Industry 4.0 expect a seamless, deterministic communication over scalable infrastructure networks that rely on a combination of wired and wireless technologies. Time-Sensitive Networking (TSN) and 5G are considered key technologies to meet the communication requirements of Industry 4.0. To integrate 5G into TSN, the 3rd Generation Partnership Project (3GPP) specified the 5G system (5GS) as Ethernet bridge, to exchange time synchronization and Quality of Service (QoS) requirement information between wired TSN entities and 5GS. QoS mapping between TSN and 5G is non-trivial and requires a deeper analysis of relevant parameters and QoS processing. In this paper, different solution approaches for QoS mapping between TSN and 5G are presented and discussed. The contribution covers fundamentals and prerequisites on the way to a 5G QoS model for TSN, which has not yet been addressed in 3GPP nor IEEE.

Zusammenfassung In diesem Beitrag wird ein alternatives, datenbasiertes Framework für die Fehlerdiagnose (FDI) basierend auf symmetrischen, positiv-definiten (SPD) Matrizen eingeführt. In der Fehlerdiagnose und Regelungstheorie enthalten SPD-Matrizen abhängig von der Analyse und Interpretation wichtige Informationen über das betrachtete System. Zur Berücksichtigung ihrer besonderen Eigenschaften wird die Riemann’sche Geometrie als mathematische Grundlage genutzt. Das grundlegende SPD-Matrix-basierte FD-Schema erlaubt eine flexible Umsetzung ohne Annahmen der statistischen Verteilung der Daten. Außerdem wird ein Überblick über mögliche Realisierungen des Frameworks für die modell- und datenbasierte FDI sowie im Bereich des Maschinellen Lernens (ML) gegeben. Es wird eine neuartige Modellierung stabiler, linearer zeitinvarianter Systeme vorgestellt und zu einem FD-Schema erweitert.

Zusammenfassung In diesem Beitrag werden zwei KI-basierte Regelungskonzepte zur automatischen Spurführung von Flurförderzeugen (FFZ) einer heterogenen Logistikflotte vorgestellt. Während die modellfreien Verfahren des bestärkenden Lernens für diese Anwendung als Vertreter der direkten neuronalen Regelung vorgestellt werden, lässt sich das zweite Regelungskonzept in die Klasse der indirekten neuronalen Regelungen einordnen. Beide Konzepte zeichnen sich dadurch aus, dass sie dazu in der Lage sind, die Reglerparameter an verschiedene Varianten von FFZ anzupassen sowie vorab bekanntes Wissen bezüglich des Regelstreckenverhaltens beim Entwurf der Regler gezielt zu berücksichtigen.

Zusammenfassung Die Automatisierung von Fahrzeuggespannen bedarf einer präzisen Manöverplanung, um das Gespann kollisionsfrei von einem initialen Zustand in einen gewünschten Zustand zu überführen. Durch Wahl einer Architektur, welche die Stärken verschiedener Planungsverfahren kombiniert, kann ein effizientes Zusammenspiel aus lokaler und globaler Trajektorienplanung erzielt werden. Die resultierende globale Lösung wird anschließend mit einem optimalen Geschwindigkeitsprofil versehen und die gewünschten Fahreigenschaften mittels Verfahren der numerischen Optimierung umgesetzt.

Alarm management systems in the process industry use root-cause analysis methods to reduce alarm logs. To enable the application of these methods in different plant types, the alarm characteristics of a continuous, two discrete, and a hybrid plant are examined. The main contribution is threefold. First, root-cause analysis requirements, posed by different plant types, are revealed. Next, existing approaches are assessed against the requirements. Since the root-cause is not necessarily the first alarm in time, its justification requires further plant knowledge. Thus, engineering documents and the necessary formalized knowledge to justify root-causes are identified.