基于自适应交互式多模型的永磁同步电机无感控制

目的 改善包装印刷机器的控制性能，减少包装印刷机器所用传感器数量和电机控制系统成本，减小包装印刷机械装置故障率和电机体积，针对传统扩展卡尔曼滤波算法中模型可能不匹配实际工况的问题，提出一种自适应多模型无感控制策略。方法 基于传统扩展卡尔曼滤波，引入多模型，在输入环节依靠状态转移概率矩阵实现多个模型间的交互，并借助隐马尔可夫模型，设计多模型的状态序列和观测序列，将观测得到的矩阵对多模型交互环节的状态转移概率矩阵进行迭代更新，提高模型面对环境扰动时的匹配程度。结果 Matlab/Simulink仿真结果表明，改进后的算法使转速的估计精度得到显著提升，同时在面对环境扰动时，其抗扰动能力显著提高。结论 与传统扩展卡尔曼滤波算法相比，改进算法提高了系统控制精度，提高了动态性能和鲁棒性，改进后算法更适合应用于包装印刷机械。

Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive Interactive Multiple Models

The work aims to propose an adaptive multi model speed sensorless control strategy to solve the problem that the models in the traditional extended Kalman filter algorithm may not match the actual working conditions so as to improve the control performance of packaging and printing machines, reduce the number of sensors used in packaging and printing machines and the cost of motor control systems, and reduce the failure rate of packaging and printing machines and the volume of motors. Based on the traditional extended Kalman filtering algorithm, multiple models were introduced to interact with each other in the input stage using a state transition probability matrix. At the same time, hidden Markov models were used to design state and observation sequences for multiple models. By iteratively updating the state transition probability matrix of the observation matrix for the interaction stage of multiple models, the matching degree of the model in the face of environmental disturbances was improved. The Matlab/Simulink simulation showed that the improved algorithm significantly improved the estimation accuracy of speed, and its anti-interference ability was significantly improved in the face of environmental disturbances. Compared with the traditional extended Kalman filter algorithm, the improved algorithm in this paper improves the accuracy of the control system, and also improves the dynamic performance and robustness, making the improved algorithm more suitable for packaging and printing machinery.