A Portable Implementation on Industrial Devices of a Predictive Controller Using Graphical Programming

Folea, Silviu; Moiş, George; Muresan, Cristina I.; Miclea, Liviu; Keyser, Robain De; Cirstea, Marcian

doi:10.1109/tii.2016.2532118

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…In those works, the authors try to identify the considerable redundancy and the strong correlations between data as well as to manage the random noises present at data. Other interesting data-driven industrial prognostics applications include [265], which presents an extended prediction self-adaptive controller employing graphical programming of industrial devices for controlling fast processes, and [266], which investigates fault prediction of power converters in industrial power conversion systems.…”

Section: Stationary Robotsmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: Stationary Robotsmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…Unlike other academic MPC strategies, the EPSAC approach is based on input-output filtering techniques, making it thus very attractive for industrial applications. The simplicity of the methodology allowed its implementation on FPGAs platforms [9] or dSpace platforms [10] and other real time operating environments [11], [12].…”

Section: A Generic Epsac-mpc Approachmentioning

confidence: 99%

Automatic tuning of predictive control in a hydrostatic drive train system in nominal operation

Ionescu

Haemers

Maxim

et al. 2019

2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)

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This paper presents an automatic model based predictive control tuning and operation of a hydrostat drive train system under nominal conditions. An industrial-friendly version of the predictive control strategy is applied whereas the model is simplified as an approximation of first order plus dead time and the control design parameters are automatically tuned. Implementation on a SISO drive train is experimentally achieved with successful performance in closed loop tests. From the identified characteristics of the real life drive train, a simulated MIMO system is extracted and automatic tuning is again performed. A limitation of the present work is that constraints are not yet included, only safety limits.

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“…thermal plants) (De Keyser and Hernandez, 2014;Dutta et al, 2014;Ionescu et al, 2016). The simplicity of the methodology allowed its implementation in FPGAs and PLC execution platforms (Folea et al, 2016).…”

Section: Prediction-based Control Mpc-epsac With Augmented Disturbancmentioning

confidence: 99%

Vibration suppression in multi-body systems by means of disturbance filter design methods

Copot

Ionescu

Vanlanduit

et al. 2017

Journal of Vibration and Control

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This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort.

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A Portable Implementation on Industrial Devices of a Predictive Controller Using Graphical Programming

Cited by 9 publications

References 28 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

Automatic tuning of predictive control in a hydrostatic drive train system in nominal operation

Vibration suppression in multi-body systems by means of disturbance filter design methods

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