Design and implementation of industrial automation control systems: A survey

Colla, Marco; Leidi, Tiziano; Semo, Mario

doi:10.1109/indin.2009.5195866

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…In practical applications, these ideas are not in use yet. PLC programming is mostly manual work and the PLC programmer is supplied with various information sources, mainly informal ones [53,54]. Process models are mostly not used when designing control programs.…”

Section: Semantic Description Of Available Datamentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

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Section: Semantic Description Of Available Datamentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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“…It was developed because the state of the practice in this domain is failing to address the needs of the market [26] and is putting too much focus on the implementation phase, with little or no activity in the earlier software lifecycle phases [8]. Other proposed approaches, among which are also MDE approaches, have not been widely adopted by industry because of their immaturity and particularly because they do not address important issues of the process control domain [9]. The MAGICS approach consists of the two development levels that are shown in Figure 1.…”

Section: Previous Workmentioning

confidence: 99%

“…This experience report should be especially interesting for practitioners dealing with not-trivial, real-life modeling languages and the corresponding tool support. It should also be interesting for the experts (researchers and practitioners) from the process control domain that are evaluating the state of the art approaches for software engineering in this domain [9].…”

Section: Introductionmentioning

confidence: 99%

The Experience of Implementing Model-Driven Engineering Tools in the Process Control Domain

Godena

Lukman

Heričko

et al. 2015

ITC

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Model-driven engineering (MDE) is a software-engineering paradigm that is being introduced into a growing number of domains. One of the most important success factors for a new MDE approach is the availability of the appropriate tool support for it. Although the literature discusses the development of support tools, only a few reports and analyses are available about the development of tool support for real-life modeling languages and MDE approaches. The goal of this paper is to fill this gap through an experience report about developing a tool-suite prototype for an MDE approach for the process control domain that is capable of supporting the development of real-life process control software. Before the work presented in this paper an initial prototype tool suite was already developed. However, it was not able to adequately support industry-scale projects. The paper starts with an analysis of the past development of this already-existing laboratory prototype and then moves on to a report about the development of the industrial prototype, which is influenced by the findings of the analysis. Then a comparison between the two prototypes is made and the lessons learned are described, which may be useful to practitioners who attempt to develop support tools for an MDE approach that are useful in practice. The most important lesson learned is that when developing tool support for complex modeling languages, the traditional development approach should not be easily rejected.

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“…*In the industrial automation field, various hardware and software architectures and accesses are currently in use for implementing control applications (Colla et al, 2009). To fulfill high control performance requirements in industry the control methods are mostly based on the proportional, integral and derivative (PID) controller which is widely used since the last few decades (Rathore et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

PLC based model predictive control for industrial process control

Aslam

2017

Int. j. adv. appl. sci

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This paper presents the real-time implementation of model predictive control technique on programmable logic controller for industrial process control. Firstly, temperature of water is regulated in liquid tank at desired temperature and then level of water in filling bottles is controlled through PWM based flow control valve. The simulations of MPC for temperature and level control are presented in MATLAB by first developing the system model of water tank. The simulation results have shown that MPC effectively regulates both temperature and level of water. Finally, MPC is real time implemented on PLC and provides better real-time control of these two variables.

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Design and implementation of industrial automation control systems: A survey

Cited by 16 publications

References 8 publications

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

The Experience of Implementing Model-Driven Engineering Tools in the Process Control Domain

PLC based model predictive control for industrial process control

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