Nonlinear modeling and control approach to magnetic levitation ball system using functional weight RBF network-based state-dependent ARX model

DOI10.1016/J.JFRANKLIN.2015.06.014zbMath1395.93258OpenAlexW843782545MaRDI QIDQ1660668

Feng Zhou, Jun Wu, Xiaoyong Zeng, Hui Peng, Yemei Qin

Publication date: 16 August 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2015.06.014

zbMATH Keywords

functional weight RBF network-based ARX modelmagnetic levitation ball systemnonlinear modeling and control

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Nonlinear systems in control theory (93C10) Design techniques (robust design, computer-aided design, etc.) (93B51) Linearizations (93B18) Realizations from input-output data (93B15)

Related Items (4)

RBF-ARX model-based robust MPC for nonlinear systems with unknown and bounded disturbance ⋮ Modeling of Maglev Yaw System of Wind Turbines and its Robust Trajectory Tracking Control in the Levitating and Landing Process Based on Ndob ⋮ Parameter identification of ARX models based on modified momentum gradient descent algorithm ⋮ Modeling and Control Approach to Coupled Tanks Liquid Level System Based on Function‐Type Weight RBF‐ARX Model

Cites Work

• A numerically stable dual method for solving strictly convex quadratic programs
• Adaptive neural control of stochastic nonlinear systems with unmodeled dynamics and time-varying state delays
• Trajectory tracking for the magnetic ball levitation system via exact feedforward linearisation and GPI control
• Time series analysis using normalized PG-RBF network with regression weights

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