基于模糊径向基函数神经网络的永磁同步电机滑模观测器设计

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。

Design of PMSM Sliding Mode Observer Based onFuzzy RBF Neural Network

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.