Control of 3d Tower Crane Based on Tensor Product Model Transformation With Neural Friction Compensation

DOI10.1002/ASJC.986zbMath1332.93083OpenAlexW1527171004MaRDI QIDQ2789945

Vinko Lešić, Šandor Ileš, Jadranko Matuško, Fetah Kolonić

Publication date: 2 March 2016

Published in: Asian Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/asjc.986

zbMATH Keywords

neural networkfriction compensationRBF network3D tower cranenon-PDC control lawonline network learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Feedback control (93B52) Neural networks for/in biological studies, artificial life and related topics (92B20) Application models in control theory (93C95) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05) Transformations (93B17)

Related Items (3)

Adaptive hierarchical sliding mode controller for tower cranes based on finite time disturbance observer ⋮ Extension of the multi-TP model transformation to functions with different numbers of variables ⋮ Influence of the Tensor Product Model Representation Of QLPV Models on The Feasibility of Linear Matrix Inequality

Cites Work

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• Friction models and friction compensation
• A survey of models, analysis tools and compensation methods for the control of machines with friction
• Globally stable direct fuzzy model reference adaptive control
• LMI-based relaxed nonquadratic stabilization conditions for nonlinear systems in the Takagi-Sugeno's form
• Tensor Product Model Transformation in Polytopic Model-Based Control
• Linear Matrix Inequalities in System and Control Theory
• A logical calculus of the ideas immanent in nervous activity

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