—One of the gunboat weapons that need to stay
stable is the cannon. Its unbalance position that caused by pitch
and roll disturbance will influence the target accuracy, target
detection, tracking system, object identification and the ability
to counter the threat. In order to determine this disturbance,
the balancing control on the movement platform can be solved
by using neural network control and sliding mode control
methods. To make an approach, the cannon movement system
can be modeled in training and elevation movements and the
disturbances are modeled through pitch and roll mechanisms.
The variations in obtained parameters of training and elevation
(moment of inertia) are the non-linearity result of the moving
cannon. The system is simulated to verify the error in the
controller’s output processed using the neural network
coordination system control and sliding mode control. The
learning process in the neural network is made using back
propagation method in order to get the weight value at the
different disturbances which their results are given in the
simulation of coordination models. On the other hand, the free
chattering of sliding mode control is implemented in order to
make the movement of training and elevation can be controlled
for having the desired angle position in the disturbance of pitch
and roll. This paper is based on the study to compare the
performance of neural network control and sliding mode
control on the moving platform.
—Cannon barrel, elevation and training, neural
network control, pitch and roll, sliding mode control.
Wiwik Wiharti is with the Department of Electronics Engineering, State
Polytechnic of Padang, Indonesia (e-mail: email@example.com).
Santi Anggraini is with the Department of Electronics Engineering,
Electronic Engineering Polytechnic Institute of Surabaya, Indonesia (e-mail:
Ihsan Lumasa Rimra is with the Department of Telecommunication
Engineering, State Polytechnic of Padang, Indonesia (e-mail:
Cite:Wiwik Wiharti, Santi Anggraini, and Ihsan Lumasa Rimra, "The Performance of Controlling Cannon Barrel Position on the Moving Platform Using Neural Network Control and Sliding Mode Control," International Journal of Computer Theory and Engineering vol. 7, no. 6, pp. 476-481, 2015.