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Mathematical Problems in Engineering
Volume 2012, Article ID 568796, 10 pages
Research Article

Radial Basis Functional Link Network and Hamilton Jacobi Issacs for Force/Position Control in Robotic Manipulation

1Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
2Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China

Received 14 April 2011; Revised 18 May 2011; Accepted 29 May 2011

Academic Editor: Shengyong Chen

Copyright © 2012 Shuhuan Wen et al. 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.


This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional (RBF) neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an external interference of the system, and it is eliminated by the robust control method. Since the conventionally fixed structure of RBF network is not optimal, resource allocating network (RAN) is proposed in this paper to adjust the network structure in time and avoid the underfit. Finally the advantage of system stability and transient performance is demonstrated by the numerical simulations.