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Sliding Mode Control With Time Delay Estimator for Boom Cranes Considering Distributed Mass Payloads

Research output: Contribution to journal › Conference article › peer-review

Abstract

Boom cranes play a significant role in various industries due to their extensive working space and high lifting capacity. Nevertheless, double-pendulum effects arise in boom cranes during heavy-load handling when the hook mass is significant. Under such conditions, the system exhibits increasingly complex dynamics, which complicates the controller design process. At present, most existing double pendulum models treat both the hook and the payload as point masses, which is no longer valid for distributed payloads and thus neglects their swing characteristics. Accordingly, this paper proposes a sliding mode control strategy combined with time-delay estimator, based on the dynamic double pendulum model of distributed payloads. The proposed method enhances robustness, achieves positioning objectives, and effectively suppressing the swing of the hook and the payload. Specifically, the system model is transformed to separate the uncertain terms. Then, the sliding surface is used to ensure that the system states converge within a finite time. Meanwhile, a time delay estimator is introduced to estimate the uncertain dynamics of the system. Finally, simulation tests are conducted through MATLAB/Simulink to validate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)251-255
Number of pages5
JournalYouth Academic Annual Conference of Chinese Association of Automation, YAC
Issue number2026
DOIs
StatePublished - 2026
Externally publishedYes
Event41st Youth Academic Annual Conference of Chinese Association of Automation, YAC 2026 - Changsha, China
Duration: 8 May 2026 → 10 May 2026

Bibliographical note

Publisher Copyright:
© 2026 IEEE.

Keywords

  • Boom Cranes
  • Distributed Mass Payloads
  • Sliding Mode Control
  • System Uncertainties
  • Time Delay Estimator

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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