现代电子技术2015，Vol.38Issue(21)：113-117,5.DOI:10.16652/j.issn.1004-373x.2015.21.030
基于遗传优化RBF神经网络的电动负载模拟器控制
Control of electric-driven load simulator based on genetic optimization RBF neural network
摘要
Abstract
For the complex nonlinearities of friction,clearance,elastic deformation,time-varying performance of the target parameters and position disturbance are existed in electric-driven load simulator of the gun control system,the conventional con-trol method can′t achieve the good static and dynamic performance indexes. In combination with the system composition and working principle of the electric-driven load simulator,the loading mathematical model was established. The RBF neural net-work controller(RBFNNC)was designed by using the position control signal of the gun control system to conduct with feedfor-ward compensation. The parameters of the controller′s weight,nodes and center vector are optimized by the improved genetic al-gorithm. The experimental results show that this control strategy can restrain the extra torque effectively,and ensure the control precision and stability when the system is loading in static or dynamic state.关键词
电动负载模拟器/RBF神经网络/遗传算法/多余力矩Key words
electric-driven load simulator/RBF neural network/genetic algorithm/extra torque分类
信息技术与安全科学引用本文复制引用
魏全增,陈机林,高强,王超..基于遗传优化RBF神经网络的电动负载模拟器控制[J].现代电子技术,2015,38(21):113-117,5.基金项目
国家自然科学基金项目(51305205) （51305205）
