Paper Title:
RBF Neural Network-Based Terminal Sliding Mode Control for Reentry Warhead
  Abstract

The existing moving mass control system of a nonspinning reentry warhead could not drive the system error to reach zero in finite time. In order to settle the finite time reach issue, an RBF neural network-based terminal sliding mode controller was presented to design the moving mass control system. It used a terminal sliding mode to ensure that the error reaches zero in finite time. The disturbance and coupled terms of the warhead were treated as uncertainties. An RBF neural network was used to estimate the uncertainties. A nonspinning warhead was taken in the simulation to test the performance of the presented controller. The simulation results show the presented controller has faster tracking speed and higher tracking precision than the former research result.

  Info
Periodical
Edited by
Helen Zhang and David Jin
Pages
381-384
DOI
10.4028/www.scientific.net/AMM.63-64.381
Citation
H. C. Zhao, J. Chen, H. Z. Liu, "RBF Neural Network-Based Terminal Sliding Mode Control for Reentry Warhead", Applied Mechanics and Materials, Vols. 63-64, pp. 381-384, 2011
Online since
June 2011
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