摘要
针对传统滑模控制易导致系统出现抖振的问题,提出了一种模糊径向基函数(RBF)神经网络滑模观测器来实现永磁同步电机(PMSM)无传感器控制。为了减小观测器系统抖振,利用模糊RBF神经网络算法动态调整滑模增益,并采用李雅普诺夫稳定性定理证明了该模糊神经网络观测器的稳定性;利用锁相环(PLL)技术提高估算精度,并削弱计算噪声。基于MATLAB/Simulink软件平台搭建了仿真模型,将模糊RBF神经网络滑模观测器系统与传统滑模观测系统进行对比。结果表明,与传统的滑模观测器相比,新型滑模观测器能够快速、有效地跟踪转子位置,精确估算出转子速度,同时具有较好的动态特性。
In view of the chattering problem which was easily caused by traditional sliding mode control,a fuzzy radial basis function(RBF)neural network sliding mode observer was proposed to realize sensorless control of permanent magnet synchronous motor(PMSM).In order to reduce the chattering of the observer system,the fuzzy RBF neural network algorithm was used to adjust the sliding mode gain dynamically,and the stability of the observer was proved by Lyapunov stability theorem.The phase locked loop(PLL)technology was used to improve the estimation accuracy and reduce the computational noise.A simulation model was built based on the MATLAB/Simulink software platform,and the fuzzy RBF neural network sliding mode observer system was compared with the traditional sliding mode observer system.The results showed that,compared with the traditional sliding mode observer,the new type of sliding mode observer could track the rotor position rapidly and effectively,and accurately estimate the rotor speed,exhibiting good dynamic characteristics.
作者
陈李济
应保胜
马强
伍娇
CHEN Liji;YING Baosheng;MA Qiang;WU Jiao(School of Automobile and Traffic Engineering,Wuhan University of Science and Technology,Wuhan 430081,China;School of Automotive and Transportation Engineering,Hubei University of Arts and Science,Xiangyang 441053,China)
出处
《电机与控制应用》
2019年第6期66-71,共6页
Electric machines & control application
基金
国家自然科学基金青年基金项目(51307047)
湖北省高等学校优秀中青年科技创新团队计划项目(T201815)
湖北省技术创新专项(重大项目)(2016AAA051)
关键词
永磁同步电机
滑模增益
滑模观测器
模糊
神经网络
锁相环
permanent magnet synchronous motor(PMSM)
sliding mode gain
sliding mode observer
fuzzy
neural network
phase locked loop(PLL)
