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The Improved Model Predictive Pitch Control Method for Wind Turbines Based on LiDAR.

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Title: The Improved Model Predictive Pitch Control Method for Wind Turbines Based on LiDAR.
Authors: Jin, Zhihao1 (AUTHOR), Fu, Dongfei1 (AUTHOR) fudongfei@ouc.edu.cn
Source: Energies (19961073). May2026, Vol. 19 Issue 9, p2194. 23p.
Subject Terms: *LIDAR, *Predictive control systems, *Reinforcement learning, *Wind turbines, *Adaptive control systems, *Wind speed measurement
Abstract: This paper presents a LiDAR-informed adaptive-cost nonlinear model predictive control (NMPC) strategy for wind turbine pitch regulation. The proposed method uses a reinforcement learning (RL) agent as a supervisory cost-shaping module that adjusts the weights in the NMPC cost function. The pitch command is obtained from the constrained NMPC optimizer, which preserves the physical prediction model, actuator limits, and receding-horizon solution structure. LiDAR-derived preview wind-speed information is used as an estimate of the incoming disturbance and is introduced into both the prediction model and the agent state. This design helps the controller account for wind-field variation over the prediction horizon and adapt the relative emphasis on power regulation, load mitigation, and pitch-action smoothness. Compared with feedforward PID (FF-PID) and fixed-weight feedforward NMPC (FF-NMPC) controllers, the proposed controller shows stronger adaptability under abrupt and stochastic wind variations in OpenFAST-MATLAB/Simulink co-simulations. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 193716090
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The Improved Model Predictive Pitch Control Method for Wind Turbines Based on LiDAR.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jin%2C+Zhihao%22">Jin, Zhihao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Dongfei%22">Fu, Dongfei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fudongfei@ouc.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 9, p2194. 23p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br />*<searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Wind+turbines%22">Wind turbines</searchLink><br />*<searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Wind+speed+measurement%22">Wind speed measurement</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper presents a LiDAR-informed adaptive-cost nonlinear model predictive control (NMPC) strategy for wind turbine pitch regulation. The proposed method uses a reinforcement learning (RL) agent as a supervisory cost-shaping module that adjusts the weights in the NMPC cost function. The pitch command is obtained from the constrained NMPC optimizer, which preserves the physical prediction model, actuator limits, and receding-horizon solution structure. LiDAR-derived preview wind-speed information is used as an estimate of the incoming disturbance and is introduced into both the prediction model and the agent state. This design helps the controller account for wind-field variation over the prediction horizon and adapt the relative emphasis on power regulation, load mitigation, and pitch-action smoothness. Compared with feedforward PID (FF-PID) and fixed-weight feedforward NMPC (FF-NMPC) controllers, the proposed controller shows stronger adaptability under abrupt and stochastic wind variations in OpenFAST-MATLAB/Simulink co-simulations. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193716090
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19092194
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 2194
    Subjects:
      – SubjectFull: LIDAR
        Type: general
      – SubjectFull: Predictive control systems
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Wind turbines
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Wind speed measurement
        Type: general
    Titles:
      – TitleFull: The Improved Model Predictive Pitch Control Method for Wind Turbines Based on LiDAR.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Jin, Zhihao
      – PersonEntity:
          Name:
            NameFull: Fu, Dongfei
    IsPartOfRelationships:
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          Dates:
            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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            – Type: volume
              Value: 19
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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