Volume 70, Issue 1

According to a commonplace saying, software is eating the world. Is software also eating control engineering? Software is currently transforming many industries including but not limited to automotive aiming at automated mobility services, agriculture moving to Agriculture 4.0, and factory automation with Industry 4.0. Software is simultaneously a flexible and universal functions enabler, and a driver of engineering complexity. At times, it seems that control engineers and software engineers are not understanding each other well enough. This article discusses two key engineering domains adjacent to control, systems and software engineering, covering engineering practice, design data exchange, and education. Finally, it suggests an extension to academic control curricula help to make control graduates even better team players.

The paper introduces a subclass of nonlinear differential-algebraic models of interest for applications. By restricting the nonlinearities to multilinear polynomials, it is possible to use modern tensor methods. This opens the door to new approximation and complexity reduction methods for large scale systems with relevant nonlinear behavior. The modeling procedures including composition, decomposition, normalization, and multilinearization steps are shown by an example of a local energy system with a nonlinear electrolyzer, a linear buck converter and a PI controller with saturation.

This paper proposes an extended Petri net formalism as a suitable language for composing optimal scheduling problems of industrial production processes with real and binary decision variables. The proposed approach is modular and scalable, as the overall process dynamics and constraints can be collected by parsing of all atomic elements of the net graph. To conclude, we demonstrate the use of this framework for modeling the moulding sand preparation process of a real foundry plant.

A fundamental measure of safety communication is the residual error probability, i. e., the probability of undetected errors. For the detection of data errors, typically a Cyclic Redundancy Check (CRC) is applied, and the resulting residual error probability is determined based on the Binary Symmetric Channel (BSC) model. The use of this model had been questioned since several error types cannot be sufficiently described. Especially the increasing introduction of security algorithms into underlying communication layers requires a more adequate channel model. This paper introduces an enhanced model that extends the list of considered data error types by combining the BSC model with a Uniformly Distributed Segments (UDS) model. Although models beyond BSC are applied, the hitherto method of the calculation of the residual error probability can be maintained.

Machine learning and particularly reinforcement learning methods may be applied to control tasks ranging from single control loops to the operation of whole production plants. However, their utilization in industrial contexts lacks understandability and requires suitable levels of operability and maintainability. In order to asses different application scenarios a simple measure for their complexity is proposed and evaluated on four examples in a simulated palette transport system of a cold rolling mill. The measure is based on the size of controller input and output space determined by different granularity levels in a hierarchical process control model. The impact of these decomposition strategies on system characteristics, especially operability and maintainability, are discussed, assuming solvability and a suitable quality of the reinforcement learning solution is provided.

In the decentralized renewable driven electric energy system, economically viable battery systems become increasingly important for providing grid-related services. End of 2016, STEAG has successfully started the commercial operation of six 15 MW large scale battery systems which have been incorporated in STEAG’s primary control pool. During the commissioning phase, extensive effort has been spent in optimizing the operational efficiency of these systems with promising results. However, the operation experience has shown that there is still significant potential for improving the system behavior as well as reducing the aging of the battery cells. By analyzing historical data of the charging power associated with the state of charge management, opportunities for significantly reducing the operational costs have been identified. By means of more involved model-based control strategies, which adequately consider the specific characteristics of the battery system, and by using mathematical optimization and artificial intelligence, adapting the state of charge management strategy to new applications, these additional cost savings can be obtained. Apart from giving insights into the operational experience with large scale battery systems, the contribution of this paper lies in proposing strategies for reducing the operational costs of the battery system using classical approaches as well as mathematical optimization and neural networks. These approaches will be illustrated by simulation results.

Zusammenfassung Der Beitrag behandelt die Entwicklung, parametrische Auslegung und Simulation einer neuen Basisregelung für einen chemischen Prozess, der zuvor teilweise manuell gefahren wurde. Die besonderen Herausforderungen bei der Lösung dieser Automatisierungsaufgabe sind eine variable Totzeit für den Zustrom eines Eduktes, eine diskrete Gasanalyse mit einer Abtastzeit von 40 Minuten und hohe Anforderungen an eine ruhige Fahrweise trotz teilweise unbekannter Störungen. Es wird an diesem Beispiel gezeigt, dass praktische Probleme dieser Art, trotz spezieller Randbedingungen, durch standardisierte Regelungsansätze, wie PI-Kaskadenregelung, Verhältnisregelungen und Störgrößenaufschaltungen gelöst werden können.

Finding and implementing a suitable machine learning (ML) solution to a task at hand has several facets. The technical side of ML has widely been discussed in detail, see, e. g., (Heizmann, M., A. Braun, M. Hüttel, C. Klüver, E. Marquardt, M. Overdick and M. Ulrich. 2020. Artificial Intelligence with Neural Networks in Optical Measurement and Inspection Systems. at – Automatisierungstechnik 68(6): 477–487). This contribution focusses on the industrial implementation issues of ML projects, particularly for machine vision (MV) tasks. Especially in small and medium-sized enterprises (SMEs), resources cannot be activated at will in order to use a new technology like ML. We take this into account by, on the one hand, helping to realistically evaluate the opportunities and challenges involved in implementing ML projects for a given task. On the other hand, we consider not only technical aspects, but also organizational, social and customer-related ones. It is discussed which know-how a company itself has to bring into an ML project and which tasks can also be performed by service providers. Here, it becomes clear that ML techniques can be used at different levels of detail. The question of “make or buy” is therefore also an entrepreneurial one when introducing ML into one’s own products and processes, and must be answered with a view to one’s own possibilities and structures.