How Can We Make Motors More Efficient with Artificial Intelligence?

Permanent magnet synchronous motors (PMSM) are crucial in many devices, but their control can be complex. A new study proposes an innovative strategy called DV-MPCC, which combines predictive current control with an improved sliding mode controller. This approach reduces computational load and improves the accuracy of voltage vector selection. Additionally, a voltage phase angle decision-making mechanism helps quickly choose the optimal vectors, making the system more efficient. Experimental tests have shown that this strategy outperforms conventional methods, offering better precision and dynamic response under various operating conditions.

Predictive current control (MPCC) is an advanced technique that predicts the future behavior of the system to make optimal control decisions. In the context of PMSM, DV-MPCC uses two control vectors to improve accuracy and reduce computation time. Sliding mode control (SMC) is a robust method for controlling nonlinear systems, known for its ability to resist external disturbances. The improved exponential reaching law uses a softsign function to balance response speed and chattering suppression, an undesirable phenomenon that can damage the system. This study demonstrates how the integration of these techniques can lead to significant improvements in motor performance.