基于自适应模糊神经网络的感应电机矢量控制  

Vector Control of Induction Motor Based on Adaptive Fuzzy Neural Network

作　　者：金爱娟[1] 邵飞旋 严紫光 JIN Aijuan;SHAO Feixuan;YAN Ziguang(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子科技》2022年第9期65-73,共9页Electronic Science and Technology

基　　金：国家自然科学基金(11502145)。

摘　　要：感应电机在传统PI控制中,参数固定且容易超调。针对该问题,文中研究了一种基于自适应模糊神经网络PI控制与全阶自适应观测器的感应电机矢量控制方案。根据感应电机数学模型建立了全阶自适应观测器的模型,采用Lyapunov稳定性理论对其进行了稳定性分析设计,并推导了转速自适应律。电机速度外环PI由自适应模糊神经网络推理系统在线整定优化,与传统控制方案相比,该方法易于实现,能够有效提高控制精准性,抑制外部扰动,节省了传感器成本。MATLAB/Simulink仿真实验表明,所提方案不仅改善了无速度传感器感应电机矢量控制系统的动态性能,还减小了外部负载扰动等情况的影响,提高了系统的自适应性和鲁棒性。In view of the problem that the induction motor has fixed parameters and is easy to overshoot in the traditional PI control,a method based on the adaptive fuzzy neural network PI control and the full-order adaptive observer is proposed in this study.According to the mathematical model of the induction motor,the model of the full-order adaptive observer is established,and the stability analysis and design are carried out using the Lyapunov stability theory,and the speed adaptive law is deduced.The motor speed outer loop PI is adjusted and optimized online by an adaptive fuzzy neural network inference system.Compared with traditional control schemes,this method is easy to implement,can effectively improve control accuracy,suppress external disturbances,and save sensor costs.MATLAB/Simulink simulation experiments show that the proposed scheme not only improves the dynamic performance of the speed sensorless induction motor vector control system,but also reduces the influence of external load disturbances,and improves the system's adaptability and robustness.

关 键 词：感应电机 无速度传感器 矢量控制 PI控制器 ANFIS 全阶自适应观测器 在线整定 鲁棒性 

分 类 号：TN787[电子电信—电路与系统]