摘要
针对神经滑模控制系统中存在的对先验数据依赖性较强的问题,结合RBF神经网络的泛化能力和自学习能力以及模糊推理算法的强适应能力,提出基于模糊RBF神经网络的永磁同步电机分数阶速度控制系统。模糊推理的引入为神经网络的不确定性提供了有效的指导作用,同时,分数阶微积分算子的引入增加了传统滑模控制器的自由度,从而对该控制器进行了进一步的优化。仿真结果表明,相比RBF神经滑模控制器,提出的模糊RBF神经分数阶滑模控制器具有更好的控制性能。
Since sliding mode control based on neural network has the problem of strong dependence on prior information,the generalization ability and self-learning ability of RBF neural network and strong adaptability of fuzzy reasoning algorithm are combined to propose the fuzzy RBF neural network based fractional order speed control system of permanent magnet synchronous motor（PMSM）. The introduction of fuzzy reasoning provides an effective guidance for the uncertainty of the neural network,and the introduction of fractional-order calculus operator can increase the degree of freedom of the traditional sliding mode controller,so as to further optimize the controller. The simulation results show that,in comparison with the sliding mode controller based on RBF neural network,the fractional order sliding mode controller based on fuzzy RBF neural network has better control performance.
作者
余潇
黄辉先
YU Xiao;HUANG Huixian(College of Information Engineering,Xiangtan University,Xiangtan 411105,China)
出处
《现代电子技术》
北大核心
2018年第11期87-90,共4页
Modern Electronics Technique
基金
湖南省教育厅重点项目资助(12A136)~~
