An internal model control strategy using artificial neural networks for a class of nonlinear systems

2003 ◽  
Author(s):  
S. Bel Hadj Ali ◽  
A. El Abed-Abdelkrim ◽  
M. Benrejeb
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Systems ◽  
Control Strategy ◽  
Internal Model ◽  
Internal Model Control ◽  
Model Control ◽  
Artificial Neural

scholarly journals Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3743
Author(s):  
Ivan Pisa ◽  
Antoni Morell ◽  
Jose Lopez Vicario ◽  
Ramon Vilanova
Keyword(s):  
Neural Networks ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Internal Model ◽  
Treatment Plant ◽  
Internal Model Control ◽  
Data Driven ◽  
Control Performance ◽  
Model Control ◽  
Artificial Neural

The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements—when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively.

An internal model control strategy for nonlinear systems

AIChE Journal ◽  
1991 ◽  
Vol 37 (7) ◽  
pp. 1065-1081 ◽  
Author(s):  
Michael A. Henson ◽  
Dale E. Seborg
Keyword(s):  
Nonlinear Systems ◽  
Control Strategy ◽  
Internal Model ◽  
Internal Model Control ◽  
Model Control
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