Adaptive neural network control for double-pendulum tower crane systems

Menghua Zhang, Xingjian Jing

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


In practical applications, tower crane systems always exhibit double-pendulum effects, because of non-ignorable hook mass and large payload scale, which makes the model more complicated and most existing control methods inapplicable. Additionally, most available control methods for tower cranes need to linearize the original dynamics and require exact knowledge of system parameters, which may degrade the control performance significantly and make them sensitive to parametric uncertainties. To tackle these problems, a novel adaptive neural network controller is designed based on the original dynamics of double-pendulum tower crane systems without any linear processing. For this reason, the neural network structures are utilized to estimate the parametric uncertainties and external disturbances. Based on the estimated information, and adaptive controller is then designed. The stability of the overall closed-loop system is proved by Lyapunov techniques. Simulation results are illustrated to verify the superiority and effectiveness of the proposed control law.

Original languageEnglish
Title of host publicationNeural Computing for Advanced Applications - 1st International Conference, NCAA 2020, Proceedings
EditorsHaijun Zhang, Zhao Zhang, Zhou Wu, Tianyong Hao
Number of pages14
ISBN (Print)9789811576690
Publication statusPublished - Aug 2020
Event1st International Conference on Neural Computing for Advanced Applications, NCAA 2020 - Shenzhen, China
Duration: 3 Jul 20205 Jul 2020

Publication series

NameCommunications in Computer and Information Science
Volume1265 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference1st International Conference on Neural Computing for Advanced Applications, NCAA 2020


  • Adaptive control
  • Double-pendulum effects
  • Lyapunov techniques
  • Neural network
  • Tower cranes

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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