作　　者： ; ;

机构地区： 华南理工大学自动化科学与工程学院

出　　处： 《计算机仿真》 2014年第1期339-343,共5页

摘　　要： 针对系统参数完全未知的感应伺服电机驱动系统,为提高系统的控制精度,通过分析间接磁场定向控制感应伺服电机驱动模型,使用RBF神经网络设计了一种新的自适应神经网络控制器,使系统的输出跟踪给定参考轨迹,并在稳定的控制过程中实现了确定学习,使分神经网络权值收敛到最优值及未知闭环系统动态的局部准确逼近,学过的知识可应用到后续相同或相似的控制任务中,提高系统的控制精度。最后,用实例仿真说明了所设计控制算法能实现在控制中学习。 In order to improve the control precision of completely unknown induction motor servo drive system and study the directional induction motor servo drive system of indirect magnetic field, an adaptive neura/controller was designed using RBF network. It not only guarantees uniformly ultimately bounded of all signals in the closed-loop system, but also achieves convergence of partial network weights to their optimal values and learning of the unknown closed-loop system dynamics in a stable control process along recurrent tracking orbit. The/earned know/edge can be used to improve control precision, and can also be recalled and reused in the same or similar control task to save time and energy. Simulation results demonstrate the effectiveness of the proposed approach.

关 键 词： 伺服电机驱动 确定学习 径向基函数神经网络 自适应神经网络控制

领　　域： [自动化与计算机技术] [自动化与计算机技术]