基于新型超螺旋滑模自适应观测器的永磁同步电机转速估计策略

Speed Estimation Algorithm for Permanent Magnet Synchronous Motor Based on Novel Super-twisting Adaptive Observer

为了提高传统基于模型参考自适应系统(model reference adaptive system,MRAS)的转速估计策略的动态性能和鲁棒性,提出了一种基于新型超螺旋滑模自适应观测器(super-twisting algorithm adaptive observer,STA-AO)的转速估计策略。首先,在MRAS结构的基础上加入了反馈校正环节,提高参考模型输出和可调模型输出之间误差的收敛速度。其次,在STA-AO中引入超螺旋滑模算法替代观测器中的PI自适应机构,提高观测器的鲁棒性。最后,通过MATLAB/Simulink仿真软件和由TMS320F28335控制的500 W永磁同步电机进行仿真和实验验证。结果表明:相比于基于MRAS的转速估计方法,在其基础上改进提出的STA-AO能使得转速估计误差更快地收敛到零,并且当电机参数发生摄动时,STA-AO对转速估计的准确性所受影响更小。因此,相比于基于MRAS的估计策略,基于STA-AO的估计策略有着更加良好的动态性能和鲁棒性。

In order to improve the dynamic performance and robustness of the traditional MRAS(model reference adaptive system)-based speed estimation strategy, we proposed a novel speed estimation strategy based on supertwisting-algorithm adaptive observer(STA-AO). Firstly, a corrective feedback loop was added to the STA-AO which is based on the structure of the model reference adaptive system(MRAS) by which the convergence speed of the difference between the outputs of the reference model and the adjustable model was improved. Then, the super twisting algorithm was introduced into an STA-AO to replace the proportional–integral(PI) adaptive mechanism, by which the robustness of the observer was improved. Finally, the simulation and experimental tests were performed with the Matlab/Simulink software and a 500 W permanent magnet synchronous motor(PMSM) controlled by the TMS320F28335 DSP. The results show that, compared with the MRAS-based speed estimation method, the STA-AO can make the speed estimation error converge to zero faster, and the speed estimation of the STA-AO is more negligibly affected by motor parameter perturbation. Therefore, the STA-AO-based estimation strategy has better dynamic performance and robustness than the MRAS-based estimation strategy.