基于智能滑模控制的永磁直线同步电机调速系统  

Research of Speed Regulating System Based on Intelligent SMC for Permanent Magnet linear synchronous Motor

作　　者：王辉[1] 万里瑞[2] 王才东[1] 

机构地区：[1]郑州轻工业学院机电工程学院,河南郑州450002 [2]河南广播电视大学机电工程系,河南郑州450008

出　　处：《电机与控制应用》2014年第2期6-10,38,共6页Electric machines & control application

基　　金：国家科技支撑计划资助项目(2012BAF12B13);河南省重点科技攻关项目(132102110057);郑州市科技攻关项目(131PPTGG411-3);郑州轻工业学院博士科研基金资助项目(000346)

摘　　要：为进一步提高永磁直线电机调速系统的动静态性能,将RBF神经网络与传统滑模变结构控制(SMC)相结合,以实现SMC控制器参数的最优化自整定。在研究永磁直线电机数学模型的基础上,开发出基于指数趋近律SMC控制器,以实现对电机速度的调节。针对常规SMC控制器参数整定困难,稳态抖振较大问题,将RBF神经网络引入常规SMC控制算法中,实现对SMC控制器参数的自寻优,并将算法应用于永磁直线电机矢量控制系统速度调节器中。仿真试验结果表明,基于RBF神经网络SMC控制算法与常规SMC控制器相比,控制系统的动静态性能更优,稳定性更好,解决了由于参数整定困难而导致SMC控制器性能不能达到最优化的问题。In order to further improve on the static and dynamic performances of the permanent magnet linear synchronous motor speed regulating system, the traditional sliding mode variable structure control （ SMC ） was combined with the RBF neural network to achieve SMC parameters self regulating. On the basis of studying the mathematic model of PMLSM, the SMC algorithm based on exponential reaching law was developed to regulate the motor speed. For solving the problem of the difficult adjustment about the SMC parameters and reducing the chattering, the RBF neural network was introduced into the SMC to realize to the self-optimization of the SMC parameters, and the new method was applied to the permanent magnet linear synchronous motor to control the motor speed. The simulation results showed the SMC controller based on RBF neural network had better static and dynamic performances and stability, and resolved the problems that the traditional SMC controller could not achieve the most optimization because of the difficulty of the parameter regulating.

关 键 词：永磁直线同步电机 滑模变结构控制 RBF神经网络 动态调整 

分 类 号：TM351[电气工程—电机]