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Soft-Constrained MPC Optimized by DBO: Anti-Disturbance Performance Study of Wheeled Bipedal Robots.

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Název: Soft-Constrained MPC Optimized by DBO: Anti-Disturbance Performance Study of Wheeled Bipedal Robots.
Autoři: Chen, Weihua, Feng, Yehao, Zhang, Tie, Peng, Canlin
Zdroj: Machines; Oct2025, Vol. 13 Issue 10, p916, 26p
Témata: PREDICTIVE control systems, OPTIMIZATION algorithms, SCIENTIFIC apparatus & instruments, ROBUST control, BIPEDALISM, FEEDBACK control systems
Abstrakt: In disturbance scenarios, wheeled bipedal robots (WBRs) require effective control algorithms to restore balance. To address the trade-off between computational burden and control precision, and to enhance anti-disturbance capability, this paper proposes a soft-constrained Model Predictive Control (MPC) algorithm with optimized horizon parameters tailored to the hardware of the WBR. A cost function is designed, and the Dung Beetle Optimizer (DBO) is employed to optimize the MPC's prediction and control horizons. An experimental platform is built, and impact and load disturbance experiments are conducted. The experimental results show that, under impact disturbances, the pitch angle and displacement overshoot with optimized MPC are reduced by 58.57% and 42.20%, respectively, compared to unoptimized LQR. Under load disturbances, the pitch angle and displacement overshoot are reduced by 17.09% and 15.53%, respectively, with both disturbances converging to the equilibrium position. [ABSTRACT FROM AUTHOR]
Copyright of Machines is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Soft-Constrained MPC Optimized by DBO: Anti-Disturbance Performance Study of Wheeled Bipedal Robots.
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Weihua%22">Chen, Weihua</searchLink><br /><searchLink fieldCode="AR" term="%22Feng%2C+Yehao%22">Feng, Yehao</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Tie%22">Zhang, Tie</searchLink><br /><searchLink fieldCode="AR" term="%22Peng%2C+Canlin%22">Peng, Canlin</searchLink>
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  Data: Machines; Oct2025, Vol. 13 Issue 10, p916, 26p
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  Data: <searchLink fieldCode="DE" term="%22PREDICTIVE+control+systems%22">PREDICTIVE control systems</searchLink><br /><searchLink fieldCode="DE" term="%22OPTIMIZATION+algorithms%22">OPTIMIZATION algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22SCIENTIFIC+apparatus+%26+instruments%22">SCIENTIFIC apparatus & instruments</searchLink><br /><searchLink fieldCode="DE" term="%22ROBUST+control%22">ROBUST control</searchLink><br /><searchLink fieldCode="DE" term="%22BIPEDALISM%22">BIPEDALISM</searchLink><br /><searchLink fieldCode="DE" term="%22FEEDBACK+control+systems%22">FEEDBACK control systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In disturbance scenarios, wheeled bipedal robots (WBRs) require effective control algorithms to restore balance. To address the trade-off between computational burden and control precision, and to enhance anti-disturbance capability, this paper proposes a soft-constrained Model Predictive Control (MPC) algorithm with optimized horizon parameters tailored to the hardware of the WBR. A cost function is designed, and the Dung Beetle Optimizer (DBO) is employed to optimize the MPC's prediction and control horizons. An experimental platform is built, and impact and load disturbance experiments are conducted. The experimental results show that, under impact disturbances, the pitch angle and displacement overshoot with optimized MPC are reduced by 58.57% and 42.20%, respectively, compared to unoptimized LQR. Under load disturbances, the pitch angle and displacement overshoot are reduced by 17.09% and 15.53%, respectively, with both disturbances converging to the equilibrium position. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Machines is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.3390/machines13100916
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      – Code: eng
        Text: English
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        PageCount: 26
        StartPage: 916
    Subjects:
      – SubjectFull: PREDICTIVE control systems
        Type: general
      – SubjectFull: OPTIMIZATION algorithms
        Type: general
      – SubjectFull: SCIENTIFIC apparatus & instruments
        Type: general
      – SubjectFull: ROBUST control
        Type: general
      – SubjectFull: BIPEDALISM
        Type: general
      – SubjectFull: FEEDBACK control systems
        Type: general
    Titles:
      – TitleFull: Soft-Constrained MPC Optimized by DBO: Anti-Disturbance Performance Study of Wheeled Bipedal Robots.
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            NameFull: Chen, Weihua
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            NameFull: Feng, Yehao
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            NameFull: Zhang, Tie
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            NameFull: Peng, Canlin
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            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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