An improved linear extended state observer-based model predictive current control for variable flux memory machine for underwater-aerial unmanned aerial vehicle applications

This paper proposes an improved linear extended state observer (ILESO) -based model predictive current control (MPCC) method for variable flux memory machine (VFMM) drives, which can be applied to underwater-aerial unmanned aerial vehicles (UA-UAVs). Due to the drastic load and speed changes during the underwater-aerial process, the magnetization state of VFMM also needs to be accordingly adjusted to permit high efficiency over a wide operational range. This will cause pronounced changes in the equivalent machine parameters, thereby affecting the robustness of the overall system. Thus, a ILESO-based MPCC method is proposed to extract the errors caused by the parameter variations in the system and compensate for the error in the driving prediction model. The ILESO parameters are easy to be tuned and implemented, while also addressing the robustness issue of the MPCC parameters. Finally, the simulation results are provided to validate the effectiveness of the proposed method, considering various operating conditions of UAVs in different environment.