摘要
在使用低精度编码器的条件下,为获得永磁同步电机良好超低速控制性能,提出了一种适用于宽转速、高噪声环境下,获得伺服驱动系统瞬时转速、角位移和等效负载转矩的在线估计方法。通过构建基于扩展卡尔曼滤波器的最优状态估计器,在线辨识系统转动惯量及迭代更新估计器系数矩阵,实现了准确、实时和稳定的状态估计。仿真和试验结果表明,该算法可获得良好的低速控制性能,并对环境噪声和系统建模误差具有良好的鲁棒性。
In order to realize the high performance speed control under ultra-low conditions with a low-resolution encoder, an online state estimation technique of servo system for instantaneous speed, position and disturbance load torque in a random noisy environment was presented. In the proposed algorithm, an optimal state estimator based on the extended Kalman filter was built. Meanwhile, the model reference adaptive system was incorporated to identify the variations of inertia moment real-timely, and the identified inertia was used to adapt the EKF for accurate, instantaneous and stable estimations. The simulation and experimental results showed a precise speed control over the low range of speeds and the proposed system was robust to modeling error and system noise.
出处
《电机与控制应用》
北大核心
2012年第9期24-29,共6页
Electric machines & control application
基金
国家自然科学基金资助项目(51075241)
山东省科技攻关项目(2009GG10004006)
关键词
超低速控制
扩展卡尔曼滤波器
状态估计
模型参考自适应系统
参数辨识
ultra-low speed control
extended Kalman filter
state estimation
model reference adaptive system
parameter identification
