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Proceedings Papers

Research on High Precision Control of Maximum Power Point Tracking for Offshore Hydraulic Wind Turbine

Jiarui Zhang, Wenting Chen, Chao Ai, Yue Yang, Shuming Shang, Qin Zhou
Proc. ASME. FPMC2022, BATH/ASME 2022 Symposium on Fluid Power and Motion Control, V001T01A030, September 14–16, 2022
Paper No: FPMC2022-89567
DOI: https://doi.org/10.1115/FPMC2022-89567
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Topics: Adaptive control, Artificial neural networks, Hydraulic drive systems, Ocean engineering, Wind turbines, Feedback, Pumps, Simulation results, Displacement, Engines
Proceedings Papers

Using Dynamic Pressure Response for Erosion Detection in Hydraulic Tubes and Hoses

Elnaz Etminan, Mahdiyar Molahasani Majdabadi, Seokbum Ko, Travis Wiens
Proc. ASME. FPMC2021, ASME/BATH 2021 Symposium on Fluid Power and Motion Control, V001T01A058, October 19–21, 2021
Paper No: FPMC2021-70511
DOI: https://doi.org/10.1115/FPMC2021-70511
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Topics: Erosion, Pressure, Pipes, Steel, Stiffness, Wave propagation, Fluids, Noise (Sound), Artificial neural networks, Computer simulation
Proceedings Papers

Machine Learning Prediction of Journal Bearing Pressure Distributions, Considering Elastic Deformation and Cavitation

Nathan Hess, Lizhi Shang
Proc. ASME. FPMC2021, ASME/BATH 2021 Symposium on Fluid Power and Motion Control, V001T01A010, October 19–21, 2021
Paper No: FPMC2021-68483
DOI: https://doi.org/10.1115/FPMC2021-68483
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Topics: Artificial neural networks, Cavitation, Deformation, Journal bearings, Machine learning, Pressure
Proceedings Papers

Study on an Underwater Flexible Manipulator Based on Hydraulic Drive

Xin Li, He Xu, Chen Yang, Haihang Wang, Fengshu Yu
Proc. ASME. FPMC2020, BATH/ASME 2020 Symposium on Fluid Power and Motion Control, V001T01A015, September 9–11, 2020
Paper No: FPMC2020-2731
DOI: https://doi.org/10.1115/FPMC2020-2731
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Topics: Artificial neural networks, Augmented reality, Flexible manipulators, Hydraulic drive systems, Muscle
Proceedings Papers

Adaptive Integral Non-Singular Terminal Sliding Mode Control of a Servo-Hydraulic System

Yashar Shabbouei Hagh, Reza Mohammadi Asl, Heikki Handroos
Proc. ASME. FPMC2020, BATH/ASME 2020 Symposium on Fluid Power and Motion Control, V001T01A054, September 9–11, 2020
Paper No: FPMC2020-2807
DOI: https://doi.org/10.1115/FPMC2020-2807
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Topics: Artificial neural networks, Servomechanisms, Sliding mode control, Stability
Proceedings Papers

A Neural Network Strategy for Learning of Nonlinearities Toward Feed-Forward Control of Pressure-Compensated Hydraulic Valves With a Significant Dead Zone

Jarmo Nurmi, Mohammad M. Aref, Jouni Mattila
Proc. ASME. FPMC2018, BATH/ASME 2018 Symposium on Fluid Power and Motion Control, V001T01A023, September 12–14, 2018
Paper No: FPMC2018-8847
DOI: https://doi.org/10.1115/FPMC2018-8847
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Topics: Artificial neural networks, Feedforward control, Pressure, Valves, Manipulators, Actuators, Brain, Signals, Simulation results
Proceedings Papers

A Hybrid Feedback Linearization and Neural Network Control Algorithm Applied to a Hydraulic Actuator

Fabio A. P. Borges, Eduardo André Perondi, Mauro A. B. Cunha, Mario R. Sobczyk
Proc. ASME. FPNI2016, 9th FPNI Ph.D. Symposium on Fluid Power, V001T01A037, October 26–28, 2016
Paper No: FPNI2016-1562
DOI: https://doi.org/10.1115/FPNI2016-1562
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Topics: Actuators, Artificial neural networks, Control algorithms, Feedback, Temperature, Cascades (Fluid dynamics), Control equipment, Friction
Proceedings Papers

Fuzzy PID and Contour Tracking as Applied to Position Control of a Pneumatic Gantry Robot

James Waldie, Brian Surgenor, Behrad Dehghan
Proc. ASME. FPMC2013, ASME/BATH 2013 Symposium on Fluid Power and Motion Control, V001T01A035, October 6–9, 2013
Paper No: FPMC2013-4457
DOI: https://doi.org/10.1115/FPMC2013-4457
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Topics: Position control, Robots, Control equipment, Algorithms, Artificial neural networks, Fuzzy logic, Robustness