1. Permanent magnet synchronous motor (PMSM) is widely used in industrial robots, new energy vehicles, aerospace, and other application fields.
2. Field oriented control (FOC) and direct torque control (DTC) are two commonly used control methods for PMSM.
3. Model predictive control (MPC) has the advantages of simple structure, fast dynamic response, and accurate control in steady state. Discrete space vector modulation (DSVM) is used to increase the number of candidate voltage vectors in MPC to further improve the control performance of motor.
The article “Model Predictive Control for PMSM Based on Discrete Space Vector Modulation with RLS Parameter Identification” provides an overview of model predictive control (MPC) for permanent magnet synchronous motors (PMSMs). The article is well-written and provides a comprehensive overview of the topic, including a discussion of FOC and DTC as well as DSVM-MPC. The article also includes references to relevant research papers that support its claims.
However, there are some potential biases in the article that should be noted. For example, the article does not discuss any potential risks associated with using MPC for PMSMs or explore any counterarguments to its claims. Additionally, while the article does provide references to relevant research papers, it does not provide any evidence or data to support its claims about the effectiveness of MPC for PMSMs. Furthermore, while the article does mention some potential benefits of using MPC for PMSMs such as fast dynamic response and accurate control in steady state, it does not provide any information about possible drawbacks or limitations associated with this approach.
In conclusion, while this article provides a comprehensive overview of model predictive control for PMSMs and includes references to relevant research papers that support its claims, there are some potential biases that should be noted such as lack of evidence or data to support its claims and lack of discussion about potential risks or drawbacks associated with this approach.