PMLSM智能控制伺服系统的仿真研究  

Simulation Research on Intelligent Control Servo System Based on PMLSM

作　　者：朱鹏涛 谭会生[1,2] 肖望勇 张发明 ZHU Pengtao;TAN Huisheng;XIAO Wangyong;ZHANG Faming(College of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou,Hunan 412007,China;College of Traffic Engineering,Hunan University of Technology,Zhuzhou,Hunan 412007,China;College of Science,Hunan University of Technology,Zhuzhou,Hunan 412007,China)

机构地区：[1]湖南工业大学电气与信息工程学院,湖南株洲412007 [2]湖南工业大学交通工程学院,湖南株洲412007 [3]湖南工业大学理学院,湖南株洲412007

出　　处：《湖南城市学院学报（自然科学版）》2020年第3期67-71,共5页Journal of Hunan City University:Natural Science

摘　　要：为提高PMLSM伺服驱动系统的控制性能,提出了一种将模糊控制、神经网络、滑模控制和自适应学习相结合的PMLSM智能补偿滑模控制算法,实现了对PMLSM伺服驱动系统的动子跟踪周期参考轨迹的高性能控制。首先,设置RBFN估值器,直接估计包括参数变化、外部扰动和非线性摩擦力在内的集总不确定度;其次,利用RBFN控制器的在线学习能力,改善模糊滑模控制器因隶属度函数缺乏学习能力而导致跟踪响应较差的问题;最后,通过增加智能鲁棒补偿控制器来提高系统鲁棒性。仿真结果表明,系统响应速度快、基本无超调、抗干扰能力强,且具有良好的动态性能和较强的鲁棒性。In order to improve the control performance of PMLSM servo drive system,an intelligent compensation sliding mode control algorithm of PMLSM is studied,which combines fuzzy control,neural network,sliding mode control and adaptive learning to realize the high performance control of PMLSM servo drive system's dynamic tracking cycle reference trajectory.At first,a RBFN estimator is set up to directly estimate the lumped uncertainty including parameter variation,external disturbance and nonlinear friction.Then,the problem of poor tracking response caused by the lack of learning ability of membership function of fuzzy sliding mode controller is improved by using the online learning ability of RBFN controller.Finally,the robustness of the system is improved by adding an intelligent robust compensation controller.The simulation results show that the system has fast response speed,almost no overshoot,strong anti-interference ability,and the system has good dynamic performance and strong robustness.

关 键 词：PMLSM 补偿滑模控制 径向基函数网络(RBFN) MATLAB/SIMULINK仿真 

分 类 号：TM351[电气工程—电机]