摘要
提出了一种基于RBF网络在线学习自适应力控制策略,将RBF网络用于X-Y定位平台的力控制回路中,利用RBF网络在线学习力控制中的不确定上界,并与反馈控制器结合,进一步确保了控制系统的稳定性,有效地提高了系统的精度和自适应能力。针对提出的控制策略,在改造后的GXY2020VP平台上进行实验验证,结果表明了该控制方法在X-Y平台力控制中的有效性。
In this paper, a kind of self-adaptive force controller based on RBF neural network is presented for constrained X-Y positioning table. In force control, the RBF neural network is applied to learning the upper bound of system uncertainties and combing it with feedback controller, the stability or robustness of the system is ensured, and the precision and adaptability are improved effectively. The control strategy proposed is applied in the force control for reformed GXY3030VP. The results of experiment demonstrate the effectiveness of the proposed controllers for X-Y position table＇s force control.
出处
《制造技术与机床》
CSCD
北大核心
2009年第4期58-62,共5页
Manufacturing Technology & Machine Tool
基金
河北省科技攻关项目(07213526)
河北省自然科学基金(F200400260)
燕山大学博士基金(B168)
