Adaptive Neural Network Tracking Control for Double-Pendulum Tower Crane Systems With Nonideal Inputs

Menghua Zhang, Xingjian Jing

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

A novel adaptive neural network tracking control method is systematically investigated for a unique double-pendulum tower crane system model in this article. Several critical and practical application-oriented control issues, including robustness, tracking error limitation, double-pendulum effects, and input dead zone nonlinearity, are considered simultaneously, which have never been well addressed in the existing literature. Technically, neural networks are employed to approximate the functions with uncertain/unknown dynamics and nonideal inputs. Several barrier Lyapunov functions are proposed that can circumvent the violation of tracking error limitations in the proposed control method. Importantly, based on the designed adaptive neural network tracking control method, the jib and trolley can track their desired trajectories very fast, and the hook and payload sway can be completely eliminated. The Lyapunov stability theory and Babalat's lemma are utilized to theoretically prove the convergence and stability of the proposed control system. Finally, well-designed simulation studies are carried out to verify the excellent performance and strong robustness of the control method. This article should be the first work considering a double-pendulum tower crane system with guaranteed convergence and performance without any linearization for the original nonlinear dynamic model.

Original languageEnglish
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Barrier Lyapunov function (BLF)
  • dead zone
  • double-pendulum effects
  • neural network
  • radial basis function
  • tower cranes

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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