Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas

Sierra-García, J. Enrique; Santos, Matilde

doi:10.4995/riai.2021.16111

Cited by 41 publications

(11 citation statements)

References 40 publications

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“…In addition, these systems may be subjected to disturbances or unknown physical parameters that may vary due to friction and degradation of AGV mechanical components. In these cases, the use of more advanced techniques such as those based on artificial intelligence (AI) and machine learning (ML) can be useful, as proved in many control engineering complex systems (Garcia‐Auñón et al, 2017; Martín et al, 2016; Sierra‐García & Santos, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

Sierra-García

Santos

2022

Expert Systems

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Section: Introductionmentioning

confidence: 99%

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

Sierra-García

Santos

2022

Expert Systems

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“…The approaches taken in the current existing literature cover a wide range of areas of specialization, depending mainly on the objectives to be achieved (Pimenta et al, 2020). The general goal has been to provide a robust and maximized energy production (Olondriz et al, 2019;Rubio et al, 2019;Sierra-García and Santos, 2020a;Sierra and Santos, 2021). More specifically, the application of structural control to offshore wind turbines has been a topic of interest the last years (Sierra-García and Santos, 2020b;Park et al, 2019;Zuo et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

TMD stroke limiting influence on barge-type floating wind turbines

2022

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“…Today, long short-term memory networks (LSTM) are widely used for time-series prediction and have been shown to be highly effective in various domains, including economics [ 4 ], energy [ 5 , 6 ], and robotics [ 7 ]. The advent of transformers [ 8 ] has ushered in a new era in the field of artificial intelligence, attracting substantial attention in various research domains, most notably in natural language processing (NLP) and computer vision and also in disciplines requiring intricate sequence analysis, such as time-series prediction [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Transformers for Multi-Horizon Forecasting in an Industry 4.0 Use Case

Vakaruk

Karamchandani

Sierra-García

et al. 2023

Sensors

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Recently, a novel approach in the field of Industry 4.0 factory operations was proposed for a new generation of automated guided vehicles (AGVs) that are connected to a virtualized programmable logic controller (PLC) via a 5G multi-access edge-computing (MEC) platform to enable remote control. However, this approach faces a critical challenge as the 5G network may encounter communication disruptions that can lead to AGV deviations and, with this, potential safety risks and workplace issues. To mitigate this problem, several works have proposed the use of fixed-horizon forecasting techniques based on deep-learning models that can anticipate AGV trajectory deviations and take corrective maneuvers accordingly. However, these methods have limited prediction flexibility for the AGV operator and are not robust against network instability. To address this limitation, this study proposes a novel approach based on multi-horizon forecasting techniques to predict the deviation of remotely controlled AGVs. As its primary contribution, the work presents two new versions of the state-of-the-art transformer architecture that are well-suited to the multi-horizon prediction problem. We conduct a comprehensive comparison between the proposed models and traditional deep-learning models, such as the long short-term memory (LSTM) neural network, to evaluate the performance and capabilities of the proposed models in relation to traditional deep-learning architectures. The results indicate that (i) the transformer-based models outperform LSTM in both multi-horizon and fixed-horizon scenarios, (ii) the prediction accuracy at a specific time-step of the best multi-horizon forecasting model is very close to that obtained by the best fixed-horizon forecasting model at the same step, (iii) models that use a time-sequence structure in their inputs tend to perform better in multi-horizon scenarios compared to their fixed horizon counterparts and other multi-horizon models that do not consider a time topology in their inputs, and (iv) our experiments showed that the proposed models can perform inference within the required time constraints for real-time decision making.

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Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas

Cited by 41 publications

References 40 publications

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories

TMD stroke limiting influence on barge-type floating wind turbines

Transformers for Multi-Horizon Forecasting in an Industry 4.0 Use Case

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