MOVING MASS ATTITUDE LAW BASED ON NEURAL NETWORKS

摘要：

Missile designers are becoming increasingly concerned with cost and cost-effectiveness.New techniques such as Moving-Mass Control Systems are being explored for their apparent cost advantage.This paper investigates the ability of a moving-mass attitude control system to control a vehicle with three-axis stabilization in intra-atmospheric space.The general nonlinear equations of motion with three internal moving masses are used to describe the coupling influence to the system caused by the relative movement of the moving masses to the vehicles shell.The rapid self-learning and adaptive capacities of radial basis function (RBF) neural networks were exploited to revise the Proportional Integral Differential (PID) controller to calculate the positions of the moving masses.The optimal solution is determined by optimizing the weights of the network through Genetic Algorithm (GA) training.With the coordination of the control, the masses are positioned independently to generate modest attitude corrections for the vehicle.Simulation results show the method to be effective in system control as static system stability is achieved to optimally adjust the missile attitude.

关键词： Moving mass Moving mass position Attitude control RBF (Radial Basis Function) PID parameter

作者: LI QIN MING YANG

作者单位: Control and Simulation Centre, Harbin Institute of Technology, Harbin, 150001, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2791-2795

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）