Peter Neumann

Bio: Peter Neumann is an academic researcher. The author has contributed to research in topics: Automation & Heterogeneous network. The author has an hindex of 9, co-authored 24 publications receiving 546 citations.

Deterministic real-time communication with switched Ethernet

Abstract: We show, under which conditions switched Ethernet can be called deterministic. The influence of the service strategies in switched Ethernet is investigated by simulation and compared with analytical bounds on the delays.

Field device integration

Abstract: Today, the large number of different device types and suppliers within a control system project makes the configuration task difficult and time-consuming. Different tools must be mastered and data must be exchanged between these tools. In the end the consistency of data, documentation and configurations can only be guaranteed by an intensive system test requiring detailed specialist knowledge. A solution can be achieved by standardizing suitable interfaces. For the computable description of device parameters the so called electronic device description language (EDDL) is specified. The electronic device description is used for the configuration and the operational behavior of a device. Furthermore, the field device tool (FDT) concept defines the interfaces between device-specific software components (device type manager-DTM) provided by the device supplier and the engineering tool of the control system manufacturer. A DTM can easily be developed using existing EDD files as shown in this article. So EDD and FDT are not contradictory technologies, but complement each other.

Field device integration in DCS engineering using a device model

Abstract: Digital based actuation and measurement along with digital communication between field devices and DCS systems require an integration strategy for the engineering process of these systems The field devices offer diagnosis, maintenance and pre-processing functions in addition to the former 4-20 mA technology This results in an allocation of functions of the DCS system into field devices This means that the design, commissioning, configuration, operator and visualisation tools perform an information flow based on a common data/information model This paper describes the requirements for a device model and its use from different agents of the DCS or supervision system The device model is specified in the object-oriented technique (with parts of UML) The device model is compared with existing device description languages (DDL) on the market like HART DDL, PROFIBUS Device Descriptions and Foundation Fieldbus DDL The device model is used by tools which transform the electronic data sheets from the field devices to the interfaces of the DCS systems This architectural view to the device description technology is discussed in this paper

Industrial Communication Protocols

Abstract: This chapter discusses a very relevant aspect in modern automation systems: the presence of industrial communication networks and their protocols. The introduction of Fieldbus systems has been associated with a change of paradigm to deploy distributed industrial automation systems, emphasizing device autonomy and decentralized decision making and control loops. The chapter presents the main wired and wireless industrial protocols used in industrial automation, manufacturing, and process control applications. In order to help readers to better understand the differences between industrial communication protocols and protocols used in general computer networking, the chapter also discusses the specific requirements of industrial applications. As the trend of future automation systems is to incorporate complex heterogeneous networks, consisting of (partially homogeneous) local and wide area as well as wired and wireless communication systems, the concept of virtual automation networks is presented

Introduction to Industrial Control Networks

Abstract: An industrial control network is a system of interconnected equipment used to monitor and control physical equipment in industrial environments. These networks differ quite significantly from traditional enterprise networks due to the specific requirements of their operation. Despite the functional differences between industrial and enterprise networks, a growing integration between the two has been observed. The technology in use in industrial networks is also beginning to display a greater reliance on Ethernet and web standards, especially at higher levels of the network architecture. This has resulted in a situation where engineers involved in the design and maintenance of control networks must be familiar with both traditional enterprise concerns, such as network security, as well as traditional industrial concerns such as determinism and response time. This paper highlights some of the differences between enterprise and industrial networks, presents a brief history of industrial networking, gives a high level explanation of some operations specific to industrial networks, provides an overview of the popular protocols in use and describes current research topics. The purpose of this paper is to serve as an introduction to industrial control networks, aimed specifically at those who have had minimal exposure to the field, but have some familiarity with conventional computer networks.

Ethernet-Based Real-Time and Industrial Communications

Abstract: Despite early attempts to use Ethernet in the industrial context, only recently has it attracted a lot of attention as a support for industrial communication. A number of vendors are offering industrial communication products based on Ethernet and TCP/IP as a means to interconnect field devices to the first level of automation. Others restrict their offer to communication between automation devices such as programmable logic controllers and provide integration means to existing fieldbuses. This paper first details the requirements that an industrial network has to fulfill. It then shows how Ethernet has been enhanced to comply with the real-time requirements in particular in the industrial context. Finally, we show how the requirements that cannot be fulfilled at layer 2 of the OSI model can be addressed in the higher layers adding functionality to existing standard protocols.

Distributed Collaborative Control for Industrial Automation With Wireless Sensor and Actuator Networks

Abstract: Wireless sensor and actuator networks (WSANs) bring many benefits to industrial automation systems. When a control system is integrated by a WSAN, and particularly if the network scale is large, distributed communication and control methods are quite necessary. However, unreliable wireless and multihop communications among sensors and actuators cause challenges in designing such systems. This paper proposes and evaluates a new distributed estimation and collaborative control scheme for industrial control systems with WSANs. Extensive results show that the proposed method effectively achieves control objectives and maintains robust against inaccurate system parameters. We also discuss how to dynamically extend the scale of a WSAN with only local adjustments of sensors and actuators.

Lyapunov-Based Model Predictive Control of Nonlinear Systems Subject to Data Losses

Abstract: In this work, we focus on model predictive control of nonlinear systems subject to data losses. The motivation for considering this problem is provided by wireless networked control systems and control of nonlinear systems under asynchronous measurement sampling. In order to regulate the state of the system towards an equilibrium point while minimizing a given performance index, we propose a Lyapunov-based model predictive controller which is designed taking data losses explicitly into account, both in the optimization problem formulation and in the controller implementation. The proposed controller allows for an explicit characterization of the stability region and guarantees that this region is an invariant set for the closed-loop system under data losses, if the maximum time in which the loop is open is shorter than a given constant that depends on the parameters of the system and the Lyapunov-based controller that is used to formulate the optimization problem. The theoretical results are demonstrated through a chemical process example.