Neuro-genetic PID autotuning

Lima, João M. G.; Azevedo, Ana Beatriz da Piedade de; Duarte, N.M.; Fonseca, Carlos M.; Ruano, A. E.; Fleming, Peter J.

doi:10.23919/ecc.2001.7076543

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…To capture the behavior of a real plant, ANNs are used -not only applications using ANN as a part of the model. 30 Also, offline training that means the ANN is trained disconnected from the real plant with historic process data is used. Lima et al 30 have given special attention to the offline training of auto-tuner models, the criterion networks for PID auto-tuning application.…”

Section: Ann Applications In Distillation For the Chemical Industrymentioning

confidence: 99%

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Artificial intelligence in process control applications and energy saving: a review and outlook

Krämer

Morgado‐Dias

2020

Greenhouse Gases

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This work summarizes selected applications of artificial neural networks and related solutions such as neuro-fuzzy within different control loops such as the network predictive control regarding energy saving. It also shows such applications in the chemical industry and points out why further research on these applications regarding the control of distillation columns might be economically promising, as the world market in the chemical industry is expected to grow from 3.47 trillion euro in 2017 to about 6.6 trillion euro in 2030, the energy consumption might also rise. As not only companies in the chemical industry set up energy saving programs to reduce energy consumption and greenhouse gases, the consumption of energy decreased since 1990 in Europe. Compared to other countries like China, the energy prices in Europe-especially in Germany-have the highest prices due to taxes and governmental issues. Thus, research about applications with artificial intelligence in energy saving holds the promise of economic gain.

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Section: Ann Applications In Distillation For the Chemical Industrymentioning

confidence: 99%

“…To capture the behavior of a real plant, ANNs are used – not only applications using ANN as a part of the model 30 . Also, offline training that means the ANN is trained disconnected from the real plant with historic process data is used.…”

Section: Ann Applications In Distillation For the Chemical Industrymentioning

confidence: 99%

Artificial intelligence in process control applications and energy saving: a review and outlook

Krämer

Morgado‐Dias

2020

Greenhouse Gases

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“…2 Here the PD mode is used to speed up response, whereas the PI mode is applied to eliminate the steady-state offset. In the last years, the classical PID scheme has been completed by autotuning devices like neural networks 3 and Fuzzy Systems 4 to adjust its parameters on-line. To resolve some specific control problems in mechanical systems, some extensions to the classical PID control scheme have been added.…”

Section: Introductionmentioning

confidence: 99%

A direct adaptive neural control scheme with integral terms

Baruch¹,

Garrido²

2004

Int. J. Intell. Syst.

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A direct adaptive neural control scheme with single and double integral-plus-state (IPS) actions is proposed. The control scheme contains two recurrent trainable neural network (RTNN) models, which are a plant parameter identifier and state estimator, an IPS feedback /feedforward controller, and one or two I-terms. The good performance of the adaptive IPS control scheme is confirmed by closed-loop systems analysis and by simulation results obtained with a MIMO plant, corrupted by noise.

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Applications of Artificial Neural Networks in Process Control Applications: A Review

Krämer

Morgado‐Dias

2018

2018 International Conference on Biomedical Engineering and Applications (ICBEA)

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Neuro-genetic PID autotuning

Cited by 3 publications

References 3 publications

Artificial intelligence in process control applications and energy saving: a review and outlook

Artificial intelligence in process control applications and energy saving: a review and outlook

A direct adaptive neural control scheme with integral terms

Applications of Artificial Neural Networks in Process Control Applications: A Review

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