Improved PMSM model considering flux characteristics for model predictive‐based current control

Abstract

Model predictive-based current control, which was proposed in our previous study, offers better current response performance by employing mathematical models of an inverter and a permanent magnet synchronous motor (PMSM). The performance of this kind of approach depends on the predictive model. From a more practical point of view, current behavior in the steady state should be improved. In our previous model predictive-based current control, the inverter model was refined by taking the dead-time into consideration. The use of the refined inverter model reduces the current offset in the control. However, the PMSM model was not investigated. This paper proposes a more appropriate PMSM model for model predictive-based current control in order to improve the current prediction in the steady state. For the purpose, we incorporate more detailed magnetic flux characteristics instead of average characteristics into the improved PMSM model. Specifically, in the improved PMSM model, the inductance of the PMSM is divided into transient and steady-state parts on the basis of magnetic saturation. The effectiveness of the improved model in the model predictive-based current control is demonstrated through simulations and experiments.

Critical Aspects of Electric Motor Drive Controllers and Mitigation of Torque Ripple—Review

“…In [31] considered MPC to minimize cost function with single controlled variable, improving steady state and dynamic performance using virtual vectors for in-wheel motors to minimize the torque ripple. In [32,96] MPCC proposed good fault tolerant capability for in-wheel motor using virtual vector compared to conventional control techniques. In [30] proposed sensor less control and-sliding mode scheme combined with duty cycle MPC for in-wheel motors which improve dynamic performance and robustness system.…”

Design of search space for improving the steady current control performance of PMSM current control system based on model predictive control

“…With the recent progress in computing performance, researchers are considering application of MPC to motor drive systems with a control period of the microsecond order 6–23 . In motor drive systems, there are two types of MPC control depending on whether independent variables to be optimized as continuous quantities or as discrete quantities.…”

“…In the former case, MPC is used to optimize voltage command (a continuous quantity), same as existing methods like current vector control using PI controllers and modulation schemes; this is called CCS‐MPC (Continuous Control Set MPC) (Figure 1 6–8 ). In the latter case, states of voltage source inverter used to apply voltage in motor drive systems are instantaneous space voltage vectors (discrete quantities), and control is performed through direct search for optimal instantaneous space voltage vectors including modulation; this is called FCS‐MPC (Finite Control Set MPC) (Figure 2 9–23 ). With FCS‐MPC, optimal independent variables are searched together with modulation, so that can choose independent variables (time‐series instantaneous space voltage vectors) impossible with existing modulation schemes.…”

Model Parameter Correction Algorithm for Predictive Current Control of SMPMSM

“…However, MRAS requires an additional adjustable model, which increased the complexity of the system. In [20] and [21], the function between inductance and flux with i d and i q was established and utilized for predictive current control to eliminate the problem of inaccurate parameters. However, inductance-current and flux-current nonlinear maps were required.…”