Using OPC technology to support the study of advanced process control

Mahmoud, Magdi S.; Sabih, Muhammad; Elshafei, Moustafa

doi:10.1016/j.isatra.2014.07.013

Cited by 38 publications

(10 citation statements)

References 17 publications

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“…Meanwhile, Kim et al developed a PLCopen OPC-UA communication component, which has verified the feasibility of the component in a real environment [15]. Mahmoud et al [16] conducted an in-depth study on OPC technology for communication in distributed control systems (DCS). Maia et al [17] investigated the transmission performance of the OPC UA communication framework as an integration element between devices.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Communication Model Based on OPC UA Wireless Network for Intelligent Production Line

Chai

Yin

et al. 2021

IEEE Access

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With the continuous development of intelligent manufacturing technology, the intelligent production line for the Industrial Internet of Things occupies an important position in the field of industrial intelligence. OPC UA is a standard for communication data exchange between intelligent production line devices. OPC UA can establish a unified information model for production line devices and improve the connectivity of heterogeneous networks. However, in industrial wireless network application scenarios, there are various types of sensing information and large amounts of data to be transmitted. Each type of data has different requirements for real-time. The traditional OPC UA communication method is difficult to achieve real-time and reliable transmission in intelligent production lines and can't meet the transmission requirements of time-sensitive data. In this paper, we propose a real-time communication model of the OPC UA wireless network for intelligent production lines (UAMPDS). IEEE 802.15.4e TSCH is used as the wireless communication infrastructure, and OPC UA protocols are fused to this model. Meanwhile, we design a load-aware time-slot scheduling algorithm to dynamically allocate and schedule time slots according to the network topology and traffic load of each node. We also implement a dynamic preemptive resource scheduling strategy based on service differentiation to ensure real-time transmission of time-sensitive data. The experimental results show that this model achieves differentiated services based on data with different latency tolerance and effectively reduces the transmission latency of real-time service data under the constrained network resources. It can avoid the starvation phenomenon of low priority queues, and improves the overall quality of service of intelligent production line communication networks.INDEX TERMS Industrial wireless network, OPC Unified Architecture, real-time, service differentiation, preemptive.

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Section: Related Workmentioning

confidence: 99%

Real-Time Communication Model Based on OPC UA Wireless Network for Intelligent Production Line

Chai

Yin

et al. 2021

IEEE Access

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“…communication BATCH systems and MES system). In addition, the software developer has to develop the IAS system; that is, programming PLC devices and HMI systems (usually SCADA systems), and establishing the communication between both systems [14].…”

Section: Iibackgroundmentioning

confidence: 99%

iMMAS an Industrial Meta-Model for Automation System Using OPC UA

Gutierrez-Guerrero¹,

Holgado-Terriza²

2017

ElAEE

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Industry 4.0 promotes the integration of IT software technology with the industrial devices of a factory for the supervision and control of Industrial Automation Systems (IAS). In this paper, a novel metamodel called iMMAS (Industrial Meta-Model for Automation System) is proposed to simplify the development and deployment of an IAS. The metamodel provides a language for modelling the industrial process of a factory through industrial devices (including PLC, Soft-PLC or RTU) using new abstractions to conceptualize the IAS. This work presents how the models are performed and executed on an OPC UA environment, and how they can be used to generate a program for a PLC. A case study is also shown to test the applicability of the proposal presented.

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“…These include: fuzzy-based control [6,22], predictor-based control [13,27], PID-based control [7,14,19,20], H ∞ filter-based control [1,5,17,21,23], faulttolerant-based control [3,9], linear quadratic regulator (LQR)-based control [8], linear matrix inequality (LMI)-based control [18], Kalman filter-based control [12,16,26], and observer-based control [11].…”

Section: Introductionmentioning

confidence: 99%

Compensation of Time-Varying Delay in Networked Control System over Wi-Fi Network

Choi

2017

INT J COMPUT COMMUN

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In this study, we design a state predictor-based output feedback controller that compensates for unavoidable time-varying network delays in networked control systems (NCSs) over Wi-Fi networks. We model time-varying network delays as timevarying input delays of NCSs over Wi-Fi networks. The designed controller consists of a linear quadratic regulator (LQR), a full-order observer, and a time-varying stepahead state predictor. The state predictor plays a key role in compensating for the time-varying input delay by providing the LQR with an estimation of future states ahead by the current network delay time. The time-varying network delays are acquired in real time by measuring the time differences between sent and received control data packets. We verify the stability and compensation performance of the designed controller by performing extensive experiments for an NCS in which a rotary inverted pendulum is controlled over Wi-Fi networks.

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Using OPC technology to support the study of advanced process control

Cited by 38 publications

References 17 publications

Real-Time Communication Model Based on OPC UA Wireless Network for Intelligent Production Line

Real-Time Communication Model Based on OPC UA Wireless Network for Intelligent Production Line

iMMAS an Industrial Meta-Model for Automation System Using OPC UA

Compensation of Time-Varying Delay in Networked Control System over Wi-Fi Network

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