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Discrete Dynamics in Nature and Society
Volume 2016, Article ID 8390529, 10 pages
Research Article

RBF Neural Network Control for Linear Motor-Direct Drive Actuator Based on an Extended State Observer

School of Traffic and Transportation Engineering, Central South University, Changsha, China

Received 27 June 2016; Accepted 16 October 2016

Academic Editor: Juan R. Torregrosa

Copyright © 2016 Zhi Liu and Tefang Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hydraulic power and other kinds of disturbance in a linear motor-direct drive actuator (LM-DDA) have a great impact on the performance of the system. A mathematical model of the LM-DDA system is established and a double-loop control system is presented. An extended state observer (ESO) with switched gain was utilized to estimate the influence of the hydraulic power and other load disturbances. Meanwhile, Radial Basis Function (RBF) neural network was utilized to optimize the parameters in this intelligent controller. The results of the dynamic tests demonstrate the performance with rapid response and improved accuracy could be attained by the proposed control scheme.