Paper Info
Title
Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations
Abstract
AbstractThe 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.
Year
DOI
Venue
2019
10.1155/2019/1480732
Periodicals
Field
DocType
Volume
Control theory,Overhead crane,Control theory,Fuzzy logic,Hoist (device),Control engineering,Artificial neural network,Mathematics,Approximation error,Payload,Sliding mode control
Journal
2019
Issue
ISSN
Citations 
1
1687-5249
0
PageRank 
References 
Authors
0.34
3
5
Name
Order
Citations
PageRank
Muhammad A. Shehu100.34
Ai-jun Li200.34
Bing Huang347121.34
Yu Wang450.74
Bojian Liu500.68