Anti-sway tracking control of tower cranes with delayed uncertainty using a robust adaptive fuzzy control

Tzu Sung Wu, Mansour Karkoub, Wen Shyong Yu, Chien Ting Chen, Ming Guo Her, Kuan Wei Wu

Research output: Contribution to journalArticle

34 Citations (Scopus)


Tower cranes are very complex mechanical systems and have been the subject of research investigations to reduce the swaying of the payload for several decades. Inherently, the dynamical model of the tower cranes is highly nonlinear and classified as underactuated. Also, the actuators are far from the payload which makes the system non-colocated. It is proposed here to use an H based adaptive fuzzy control technique to control the swaying motion of a tower crane. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating the dynamics of the tower crane with an online update law. The proposed robust control law for payload positioning is based on a variable structure (VS) adaptive fuzzy control scheme. The adaptive fuzzy control technique fuses a VS scheme and it is derived based on a Lyapunov criterion and the Riccati-inequality. The control design overcomes modeling inaccuracies, such as drag and friction losses, effect of time delays from backlash, as well as parameter uncertainties and compensate for the effect of the external disturbances on tracking error such that all the states of the system are uniformly ultimately bounded (UUB). Therefore, the H tracking performance can be achieved such that the payload swing is reduced to as small as possible when the payload is moved from point to point. Simulations show that the proposed control scheme is effective in reducing payload swing in the presence of uncertainties, time delays, and external disturbances.

Original languageEnglish
Pages (from-to)118-137
Number of pages20
JournalFuzzy Sets and Systems
Publication statusPublished - 1 May 2016



  • Adaptive fuzzy control
  • H control
  • Time delays
  • Tower crane systems
  • Variable structure scheme

ASJC Scopus subject areas

  • Artificial Intelligence
  • Logic

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