CONTROL OF 3D TOWER CRANE BASED ON TENSOR PRODUCT MODEL TRANSFORMATION WITH NEURAL FRICTION COMPENSATION

被引：43

作者：

Matusko, Jadranko ^{[1
]}

Iles, Sandor ^{[1
]}

Kolonic, Fetah ^{[1
]}

Lesic, Vinko ^{[1
]}

机构：

[1] Univ Zagreb, Fac Elect Engn & Comp, Dept Elect Machines Drives & Automat, Zagreb 41000, Croatia

来源：

ASIAN JOURNAL OF CONTROL | 2015年 / 17卷 / 02期

关键词：

3D tower crane; neural network; non-PDC control law; friction compensation; RBF network; on-line network learning; SCHEDULING FEEDBACK-CONTROL; APPROXIMATION; STABILIZATION; DESIGN;

D O I：

10.1002/asjc.986

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：443 / 458

页数：16

共 47 条

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