Haoyang Li

ORCID: 0009-0001-5324-0476
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About
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Research Areas
  • Hydraulic and Pneumatic Systems
  • Underwater Vehicles and Communication Systems
  • Fault Detection and Control Systems
  • Wave and Wind Energy Systems
  • Control Systems in Engineering
  • Advanced Algorithms and Applications
  • Fluid Dynamics and Vibration Analysis
  • Industrial Technology and Control Systems
  • Adaptive Control of Nonlinear Systems
  • Machine Fault Diagnosis Techniques
  • Robotic Path Planning Algorithms
  • Wind Energy Research and Development

Southwest University of Science and Technology
2024

Norwegian University of Science and Technology
2020

Shanghai University of Electric Power
2009

Abstract Phase I of the OC6 project is focused on examining why offshore wind design tools underpredict response (loads/motion) OC5-DeepCwind semisubmersible at its surge and pitch natural frequencies. Previous investigations showed that underprediction was primarily related to nonlinear hydrodynamic loading, so two new validation campaigns were performed separately examine different load components. In this paper, we validate a variety against test data, focusing ability accurately model...

10.1088/1742-6596/1618/3/032033 article EN Journal of Physics Conference Series 2020-09-01

This article investigates the underwater navigation control of a water–air amphibious multirotor vehicle. We use active disturbance rejection (ADRC) to construct tandem-level ADRC motion controller for vehicle and introduce particle swarm optimization (PSO) quickly tune parameters. First, vehicle's governing kinematic dynamic equations are derived. Then, hydrodynamics process is analyzed estimated. Accordingly, ADRC-based position attitude controllers designed compared with traditional...

10.1109/joe.2024.3353413 article EN IEEE Journal of Oceanic Engineering 2024-03-11

Because a serious fault would result in reduced amount of electricity supply power plant, the real-time diagnosis system is extremely important for steam turbine generator set. A novel intelligent proposed by using fuzzy cerebellar model articulation controller (CMAC) neural network to detect and identify faults failures critical components. framework described. The CMAC built analysed detail step step. case including three simulated with CMAC. results show that diagnostic high accuracy,...

10.1243/09544062jmes1248 article EN Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science 2009-05-01
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