Comparative analysis of neural-network and fuzzy auto-tuning sliding mode controls for overhead cranes under payload and cable variations
type: Item
instance of: scholarly article
title: Comparative analysis of neural-network and fuzzy auto-tuning sliding mode controls for overhead cranes under payload and cable variations
zbMATH Open document ID: 1416.93047
DOI: 10.1155/2019/1480732
author: Muhammad A. Shehu
author: Bing Huang
author: Yu Wang
author: Bojian Liu
author: Ai-Jun Li
author: Le Anh Tuan
published in: Journal of Control Science and Engineering
publication date: 2019-06-26T00:00:00Z
review text: Summary: The overhead crane is required to operate fast and precisely with minimal sway. However, high-speed operations cause undesirable load sways, hazardous to operating personnel, the payload being handled, and the crane itself. Thus, a high-quality control is required. In this work, the nonlinear model of the overhead crane was established and the sliding mode control (SMC) was proposed that ensured the existence of sliding motion in the presence of payload and hoisting height variations, and viscous frictions. To maximize the benefits derived from the proposed control method, novel sliding slope-update based on intelligent neural-network and fuzzy algorithms were developed to tune the controller, guaranteeing precise tracking of the actuated variables as well as regulation of the unactuated variables. The proposed methods adjust predetermined value of the sliding manifold's slope in response to variations in hoisting heights. Control applications were conducted, and results based on graphical, integral absolute error (IAE), and integral time absolute error (ITAE) proved the effectiveness of the proposed algorithms. It was observed that the response of the controller with back-propagation-trained neural-network was more effective relative to that of the fuzzy algorithm.
Mathematics Subject Classification ID: 93B12
Mathematics Subject Classification ID: 93C42
Mathematics Subject Classification ID: 93B40
Mathematics Subject Classification ID: 93C95
zbMATH DE Number: 7073707
zbMATH Keywords: fuzzy auto-tuning sliding mode controls
zbMATH Keywords: overhead cranes
zbMATH Keywords: neural networks
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2019/1480732
OpenAlex ID: W2907532541
cites work: Hierarchical Sliding Mode Control for Under-actuated Cranes
cites work: Learning representations by back-propagating errors
description: scientific article; zbMATH DE number 7073707