Most promising control scheme for permanent magnet synchronous motor

Electric machines consume nearly half of all electricity generated worldwide, making them one of the top contributors to carbon dioxide emissions. Therefore, if we want to develop sustainable societies, we must ensure that the electric motors we use are highly efficient and reliable.

The world of electric motorcycle however, vast and varied. Permanent magnet synchronous motors (PMSMs) offer several key advantages over competing technologies due to their small size, efficiency, and high power factor. These qualities have earned PMSM a place in electric vehicle powertrains, high-speed trains, electric train propulsion and advanced numerical control machining systems, among other applications.

For the PMSM to work as intended, using a proper control strategy is just as important as the engine components themselves. In this regard, model-driven predictive control (MPC) is the most attractive strategy today. Simply put, MPC involves having a mathematical model of the dynamic process one is intended to control and using the predictions of this model to modulate the control signals. The MPC approach allows one to maintain a PMSM operating within a desired set of constraints while taking advantage of well-established transmission system and power converter models.

In particular, the finite control set MPC strategy (FCS-MPC) is one of the more intuitive and simple ways to implement MPC in PMSM, and has become a hotbed in MPC research. However, there are still many challenges in the FCS-MPC that still need to be addressed before this control program can realize the full potential of the PMSM.

To this end, a team of researchers led by Professor Xiaodong Sun of Jiangsu University, China, recently published a review paper in the journal Science. IEEE/CAA Journal of Automatica Sinica in September 2022. In it, they explain the basic principles of FCS-MPC, its disadvantages along with modern techniques developed to mitigate them, and future research directions. .

Some of the more prominent challenges in FCS-MPC that apply to PMSM are Complicated calculations and the delay associated with the predictions of the underlying model and the optimization algorithm that determines the appropriate values ​​for the control signals. Furthermore, FCS-MPC calculations based on multiple model parameters must be set up correctly for the predictions to be meaningful.

The article begins by addressing potential challenges and solutions with the aim of making FCS-MPC performance more resilient to parameter fluctuations and uncertainties as well as external disturbances. . Some studies have focused on estimating possible disturbances and offsetting them, while other studies have attempted to remove the effect of parameter fluctuations through new twists on the models. predictive models, such as non-parametric predictive models or online parameter recognition methods.

The paper also mentions the latest progress achieved in reducing the computational burden of FCS-MPC. The authors describe other attractive schemes to improve performance, such as a multi-vector FCS-MPC strategy, which offsets the latency introduced by MPC calculations, and weight factor correction applied to the variables that the system has to control, implementing the idea of ​​priority.

Finally, the paper outlines some important research directions to enable the application of FCS-MPC in PMSM. One of them is a multi-step control strategy, which can greatly reduce switching losses and make the overall system more efficient. Another involves enhancing important parts of FCS-MPC theory, such as stability analysis, as well as discovering new ways to combine different control schemes to obtain better performance.

In this regard, Professor Sun said, “The recent strong development of new methods makes it possible for PMSM drive systems to combine solutions, such as fault-tolerant control and needless control. sensors, in practical applications. Further research will be needed to explore the combination of these approaches for better results.”

In short, improving MPC techniques can help us operate PMSM more effectively in any given context, no matter how challenging the operating conditions. In turn, this will turn into social benefits Professor Sun explains in various aspects: “Model-driven predictive control aims to solve control problems with optimization requirements. Thus, with the deepening of MPC research, the life of people will become more economical, safe and environmentally friendly.”

Provided by China Automation Association