| Modelling engineering systems using analytical and neural techniques: Hybridization JE Sierra, M Santos Neurocomputing 271, 70-83, 2018 | 58 | 2018 |
| Improving wind turbine pitch control by effective wind neuro-estimators JE Sierra-García, M Santos IEEE Access 9, 10413-10425, 2021 | 48 | 2021 |
| Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control JE Sierra-Garcia, M Santos Neural Computing and Applications 34 (13), 10503-10517, 2022 | 45 | 2022 |
| Performance analysis of a wind turbine pitch neurocontroller with unsupervised learning JE Sierra-García, M Santos Complexity 2020, 1-15, 2020 | 42 | 2020 |
| Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas JE Sierra-García, M Santos Revista Iberoamericana de Automática e Informática industrial 18 (4), 327-335, 2021 | 37 | 2021 |
| Deployment and evaluation of an industry 4.0 use case over 5G W Nakimuli, J Garcia-Reinoso, JE Sierra-Garcia, P Serrano, IQ Fernández IEEE Communications Magazine 59 (7), 14-20, 2021 | 37 | 2021 |
| Wind turbine pitch reinforcement learning control improved by PID regulator and learning observer JE Sierra-Garcia, M Santos, R Pandit Engineering Applications of Artificial Intelligence 111, 104769, 2022 | 36 | 2022 |
| Wind and payload disturbance rejection control based on adaptive neural estimators: application on quadrotors JE Sierra, M Santos Complexity 2019, 2019 | 33 | 2019 |
| Exploring reward strategies for wind turbine pitch control by reinforcement learning JE Sierra-García, M Santos Applied Sciences 10 (21), 7462, 2020 | 31 | 2020 |
| Mechatronic modelling of industrial AGVs: A complex system architecture JE Sierra-García, M Santos Complexity 2020, 1-21, 2020 | 30 | 2020 |
| Switched learning adaptive neuro-control strategy JE Sierra-García, M Santos Neurocomputing 452, 450-464, 2021 | 27 | 2021 |
| Lookup table and neural network hybrid strategy for wind turbine pitch control JE Sierra-García, M Santos Sustainability 13 (6), 3235, 2021 | 22 | 2021 |
| UWB-based safety system for autonomous guided vehicles without hardware on the infrastructure L Zamora-Cadenas, I Velez, JE Sierra-Garcia IEEE Access 9, 96430-96443, 2021 | 21 | 2021 |
| Neural networks and reinforcement learning in wind turbine control JE Sierra-García, M Santos Revista Iberoamericana de Automática e Informática Industrial 18 (4), 327-335, 2021 | 21 | 2021 |
| Transporte multi-AGV de una carga: estado del arte y propuesta centralizada F Espinosa, C Santos, JE Sierra-García Revista Iberoamericana de Automática e Informática industrial 18 (1), 82-91, 2020 | 20 | 2020 |
| Intelligent control of an UAV with a cable-suspended load using a neural network estimator JE Sierra-García, M Santos Expert Systems with Applications 183, 115380, 2021 | 19 | 2021 |
| Forecasting automated guided vehicle malfunctioning with deep learning in a 5G-based industry 4.0 scenario S Vakaruk, JE Sierra-García, A Mozo, A Pastor IEEE Communications Magazine 59 (11), 102-108, 2021 | 18 | 2021 |
| Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories JE Sierra‐Garcia, M Santos Expert Systems 41 (2), e13076, 2024 | 14 | 2024 |
| Modelado de un AGV híbrido triciclo-diferencial R Sánchez, JE Sierra-García, M Santos Revista Iberoamericana de Automática e Informática industrial 19 (1), 84-95, 2022 | 14 | 2022 |
| Evolutive tuning optimization of a PID controller for autonomous path-following robot MR Abajo, JE Sierra-García, M Santos 16th International Conference on Soft Computing Models in Industrial and …, 2022 | 12 | 2022 |
Matilde Santos PeñasFull Professor of Computer Science and Physics, Complutense University of MadridDirección de correo verificada de ucm.es
