Control of 3d Tower Crane Based on Tensor Product Model Transformation With Neural Friction Compensation. Issue 2 (12th September 2014)

• Record Type: Journal Article
• Authors: Matuško, Jadranko; Ileš, Šandor; Kolonić, Fetah; Lešić, Vinko; Baranyi, Péter; Yam, Yeung
• Is Part Of: Asian journal of control. Volume 17:Issue 2(2015:Mar.)
• Page Start: 443
• Page End: 458
• Publication Date: 2014-09-12
• Subjects: Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.986
• Languages: English
• ISSNs: 1561-8625

Abstract

<abstract abstract-type="main" id="asjc986-abs-0001"> <title>Abstract</title> <p id="asjc986-para-0005">Fast and accurate positioning and swing minimization of heavy loads in crane manipulation are demanding and, at the same time, conflicting tasks. Accurate load positioning is primarily limited by the existence of a nonlinear friction effect, especially in the low speed region. In this paper the authors propose a new control scheme for 3D tower crane, that consists of a tensor product model transformation based nonlinear feedback controller, with an additional neural network based friction compensator. Tensor product based controller is designed using linear matrix inequalities utilizing a parameter varying Lyapunov function. Neural network parameters adaptation law is derived using Lyapunov stability analysis. The simulation and experimental results on a 3D laboratory crane model are presented.</p> </abstract>