Open AccessRBF Neural Network Adaptive Robust Sliding Mode Control Method of Artillery Ammunition Transfer Arm
Author(s) -
Hangjun Cai,
Longmiao Chen
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/446/2/022003
Subject(s) - control theory (sociology) , artificial neural network , transfer function , trajectory , nonlinear system , artillery , tracking error , pid controller , radial basis function , servomechanism , servo , engineering , computer science , control engineering , artificial intelligence , control (management) , physics , temperature control , electrical engineering , quantum mechanics , astronomy
This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network for a kind of value controlled asymmetric cylinder electro-hydraulic servo system of an ammunition transfer arm in the presence of uncertain nonlinearity and parameter uncertainty. On the premise of setting the expected trajectory, using RBF neural networks to approximate unknown parameters, by setting appropriate neural network parameters and adaptive terms, the change trend of position parameter was estimated. The stability of close loop system is verified by the Lyapounov theory. Compared with the simulation results of PID control method and expected trajectory, RBF Neural Net sliding mode control method has smaller system tracking error, faster response and better tracking performance.