摘要
描述了X-Y平台实验系统的组成及其模型的建立过程。针对X-Y定位平台中存在的载荷以及摩擦参数的不确定性问题,提出了一种基于改进神经网络来补偿X-Y定位平台不确定性的方法。采用自适应遗传算法调整神经网络的权值系数,增强了神经网络的学习能力,使该定位平台的控制精度、鲁棒性和动态特性得到了改善。计算机仿真结果表明这种控制设计方案具有很好的动态特性。
The X - Y table experiment system and its modeling were described. An improved neural networks was proposed to deal with the uncertainty of load and friction parameters existed in the X - Y positioning table. An adaptive genetic algorithm was proposed to update the parameters of the neural network so as to enhance the control precision and improve the robustness of the system. Simulation results show that this control scheme is of very good performances.
出处
《机床与液压》
北大核心
2006年第6期187-189,共3页
Machine Tool & Hydraulics
基金
河北省自然科学基金资助项目(F2004000260)
