Title: RBF neural network-based sliding mode control for a ballistic missile

Authors: Hongchao Zhao, Wenjin Gu, Ruchuan Zhang

Addresses: Department of Strategic Missile Engineering, Naval Aeronautical and Astronautical University, No. 188, Erma Road, Yantai City, Shandong Province, 264001 P.R. China. ' Department of Control Engineering, Naval Aeronautical and Astronautical University, No. 188, Erma Road, Yantai City, Shandong Province, 264001 P.R. China. ' Graduate Students' Brigade, Naval Aeronautical and Astronautical University, No. 188, Erma Road, Yantai City, Shandong Province, 264001 P.R. China

Abstract: In this paper, the non-linear models for the three channels of a ballistic missile are analysed. The coupled terms are taken as additional disturbances for every single channel, in order to realise the independent design for every channel and to simplify the structure of the control system. An RBF neural network-based sliding mode controller is designed for every channel|s thrust vector control system of the ballistic missile. For the controller, the RBF neural network modifies the parameter of the sliding mode controller to approximate the lumped uncertainty. The performance of the designed RBF neural network-based sliding mode controller is compared with that of the conventional PID controller in the numerical simulation, and its effectiveness is demonstrated by the simulation results.

Keywords: ballistic missiles; radial basis function; RBF neural networks; sliding mode control; SMC; thrust vector control; TVC; nonlinear modelling; simulation.

DOI: 10.1504/IJMIC.2009.029022

International Journal of Modelling, Identification and Control, 2009 Vol.8 No.2, pp.107 - 113

Available online: 27 Oct 2009 *

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