目的 为了提高包装生产线中永磁同步电机（PMSM）的控制性能,降低对霍尔传感器的依赖,构建高精度、低成本的无感控制系统,提出一种基于改进伞蜥优化算法（IFLO）与UKF结合的PMSM无位置传感器控制算法。方法 首先,采用基于Sine混沌映射的透镜反向学习机制对FLO算法进行改进,解决FLO算法中初始种群多样性不足的问题;其次,引入基于Lévy分布的自适应鹈鹕水面飞行机制,解决FLO存在的精度不足和易陷入局部解等问题;最后,利用IFLO算法优化UKF的过程噪声协方差矩阵Q和测量噪声协方差矩阵R,缩小转子转速的跟踪误差。结果 IFLO算法的有效性已通过基准函数测试得以验证。仿真结果显示,IFLO-UKF方法在转速估计中,其稳态均值绝对误差为2.07 rad/min;在位置估计中,其稳态均值绝对误差为0.0197 rad。与现有方法相比,速度估计误差缩小至原来的29.4%,位置估计误差缩小至原来的23.8%。结论 所提出的IFLO-UKF融合策略在提升无位置传感PMSM控制精度方面展现出良好效果,具有较强的应用潜力。
Abstract
To enhance the control performance of permanent magnet synchronous motors (PMSMs) in packaging production lines, reduce reliance on Hall sensors, and construct a high-precision, low-cost sensorless control system, the work aims to propose a PMSM sensorless control algorithm based on the improved Flora Optimization (IFLO) algorithm combined with Unscented Kalman Filter (UKF). Firstly, the initial population diversity issue in the FLO algorithm was addressed by incorporating a lens reverse learning mechanism based on Sine chaotic mapping. Secondly, an adaptive pelican surface flight mechanism based on Levy distribution was introduced to overcome the performance bottlenecks of FLO in terms of insufficient accuracy and susceptibility to local optima. Finally, the IFLO algorithm was utilized to optimize the Q and R of UKF, thereby reducing the tracking error of rotor speed. The effectiveness of the IFLO algorithm was verified through benchmark function tests. Simulation results showed that the IFLO-UKF method had a steady-state mean absolute error of 2.07 rad/min in speed estimation and 0.019 7 rad in position estimation. Compared with existing methods, the speed estimation error was reduced to 29.4% of its original value, and the position estimation error was reduced to 23.8% of its original value. The proposed IFLO-UKF fusion strategy demonstrates excellent performance in improving the control accuracy of sensorless PMSMs and holds significant application potential.
关键词
永磁同步电机 /
无迹卡尔曼滤波 /
噪声协方差矩阵优化 /
伞蜥优化算法
Key words
permanent magnet synchronous motor /
unscented Kalman filter /
noise covariance matrix optimization /
frilled lizard optimization
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