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.