摘要
齿隙和摩擦是齿轮传动系统中制约运动控制精度不可避免的非线性现象,常规的PID控制难以达到较好的控制品质,变结构控制是解决非线性系统控制问题的一种有效方法。基于滞环齿隙模型和集合摩擦模型,建立了齿轮传动系统动力学模型。采用径向基函数(Radial Basis Function,RBF)神经网络和变结构原理构成复合控制器,对系统齿隙、摩擦进行了补偿。仿真分析了分别采用PID控制、增益固定变结构控制以及RBF神经网络变结构控制的补偿效果。结果表明,RBF神经网络变结构控制降低了增益固定变结构控制的抖振现象,控制精度优于PID控制。
Backlash and friction are the non-linearities arising in almost all the gear driving systems. It is difficult to meet the control request with the traditional PID control. Variable structure control is a valid approach to deal with non-linearity questions. The variable structure dynamic model of gear driving system was established based on backlash hysteresis model and friction aggregation model. The influence of backlash and friction was compensated based on RBF neural network variable structure compound controller. The effectiveness of PID control, variable structure control and RBF neural network variable structure control were simulated and compared. Results suggest that RBF neural network variable structure control can reduce the buffeting of variable structure control, and the precision is better than PID control.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2009年第18期5858-5860,共3页
Journal of System Simulation
关键词
齿隙
摩擦
补偿
RBF神经网络
变结构控制
backlash
friction
compensation
RBF neural network
variable structure control
