Jörn Peschke

AutomationML - the glue for seamless automation engineering

This contribution presents the basic architecture of the neutral data format AutomationML developed by the companies Daimler, ABB, Siemens, Rockwell, Kuka, Zuhlke, netAllied and the universities of Magdeburg and Karlsruhe. AutomationML serves for the data exchange between manufacturing engineering tools and therefore supports the interoperability between them. It covers information about the plant structure (topology and geometry) and the plant behaviour (logic and kinematics). The first version of AutomationML has been presented at the Hannover fair in 2008.

Formal models for the verification of IEC 61499 function block based control applications

Industrial automation is currently on the cusp to the application of distributed systems based on distributed intelligence enabling distributed decision making within control. As one main technology within this field IEC 61499 based function block systems would be applied. But the usage of function block systems may cause problems which have to be avoided prior to the integration of a function block system in a control system. Hence, function block systems need to be examined using formal models and formal verification technologies. Therefore, formal models need to be created automatically during the function block system design. Within this paper an algorithm for automatic model design and its integration in the function block system designer would be described

Networked priced timed automata for energy-efficient factory automation

Energy efficiency and energy savings are urging and intensively discussed topics in the domain of industry automation because of environmental, social and legal reasons [5]. Factory automation can significantly contribute to energy efficiency in manufacturing. However, in order to take advantage of energy saving potentials, a specific energy system model is necessary. In this paper, we propose a formal energy system model based on timed automata with energy efficiency extensions (PTA), that incorporates operating and transitional modes annotated with timing and process-related constraints, input power and utility. We are interested in the verification and calculation of switching sequences satisfying domain specific requirements to handle and actively switch energy-enabled automation systems. Furthermore, the motivation is to synthesize energy-efficient switching schedules for automated manufacturing systems. In this context, a real-world factory automation use case illustrates and provides proof-of-concept for this approach.

Control programming using Java

Networking and distributed applications have an increasing relevance in industrial automation as the horizontal and vertical integration of control systems progresses. In this context, object-oriented programming concepts and languages like Java become more and more interesting for all levels of automation. Within this article the state of the art in real-time Java and its applicability in the automation domain are discussed and an example for an implementation of a run-time environment utilizing the real-time specification for Java (RTSJ) will be introduced.

Distributed control programming in Java - The JAKOBI system

Within this paper the design and the implementation of a component based framework for distributed control applications based on Java will be presented. It has been developed over the course of the JAKOBI project. This project targets the applicability of Java in control applications using extensions such as the RTSJ specification. It introduces concepts similar to IEC 61499 allowing the execution of Java based applications within real-time constraints without loosing advantages like dynamic class loading or platform independence. The usage of these concepts opens a wide range of possibilities for future control applications like reconfiguration during runtime or vertical integration without conceptual and technological breaks.

Efficient identification of energy-optimal switching and operating sequences for modular factory automation systems

In order to enable energy-efficient operation of factory automation systems during nonproductive (idling) phases, the energy-optimal sequence of operating modes has to be calculated. Due to modular structures and runtime constraints, the combinatorial optimization problems that have to be solved to calculate energy-minimizing schedules for todays automation systems become extremely complex. In this paper, a novel domain-specific branch-and-bound algorithm is proposed that takes the structural knowledge about the automation system into account in order to attenuate the exponential complexity. Relying on a network of automation subsystems representing the switching and energetic operating behavior of the automation system, the method uses the minimal energy demand for unrelated subsystems, which can be efficiently calculated using off-the-shelf constraint optimization techniques, as lower bounds for the energy demand of the complete automation system. Evaluations indicate that the proposed procedure outperforms a complete enumeration in terms of computational time by more than 70 percent while assuring to identify the energy-optimal switching and operating sequences.

An Artificial Intelligence Approach for Online Optimization of Flexible Manufacturing Systems

This paper addresses the problem of efficiently operating a flexible manufacturing machine in an electricity micro-grid featuring a high volatility of electricity prices. The problem of finding the optimal control policy is formulated as a sequential decision making problem under uncertainty where, at every time step the uncertainty comes from the lack of knowledge about fu-ture electricity consumption and future weather dependent energy prices. We propose to address this problem using deep reinforcement learning. To this purpose, we designed a deep learning architecture to forecast the load profile of future manufacturing schedule from past production time series. Combined with the forecast of future energy prices, the reinforcement-learning algorithm is trained to perform an online optimization of the production ma-chine in order to reduce the long-term energy costs. The concept is empirical-ly validated on a flexible production machine, where the machine speed can be optimized during the production.