# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### Deadbeat Predictive Control of PMSM
#### 3.1.2 Robust Control Strategy  
DPC relies on accurate discrete models, and system disturbances caused by parameter mismatches and load mutations will seriously affect the performance of motor control [72; 73]. In order to solve this problem, the main research directions can be summarized into two groups. One is online parameter identification, to identify changes in parameters in real-time, and bring real parameter values into the control system to improve the accuracy [74; 75]. Reference [76] proposed an online parameter identification method based on the Kalman filter algorithm to identify the parameters after decoupling the inductance and permanent magnet flux linkage. The identification accuracy was high, while the complexity was simplified and the multi-load load was significantly reduced. Reference [77] proposes a multi-parameter identification method for high-frequency sinusoidal voltage injection when the motor is running at low speed, which not only takes into account both online and offline identification, but also avoids the coupling effect between multiple parameters. It effectively guarantees the identification accuracy of steady-state and transient resistance and flux linkage, and has a good identification effect. In [78], an improved DPCC with parameter identification is proposed. The parameter identification method was based on the reconstructed eigenvector of the current injection disturbance observer. Both the low-frequency band and the high-frequency band could ensure zero error in the steady-state current. In [79], a method combining the deadbeat control and adaptive control methods is proposed. Reference [80] proposes a motor parameter identification strategy using the autoregressive least square method to adjust the deadbeat flow controller parameter. In [81], a method of citing the Lyapunov function to eliminate the current tracking error, which needs to control the rate of change of the Lyapunov function to be less than zero, is proposed. Reference [82] proposes a method to deal with sampling error through the sensitivity function. The other type of research direction avoids the loss of robustness due to parameter mismatch and current tracking error through disturbance compensation by introducing a disturbance observer into the traditional DPCC [83]. Table 2 compares the advantages of existing disturbance observers. In [84], a current and disturbance observer was designed which could not only reduce the current harmonics caused by parameter mismatch, but also suppress the influence caused by current measurement errors through compensation. Based on the ideal model, Ref. [85] combined a sliding mode velocity controller and discrete DPCC. Furthermore, a high-order sliding mode observer was designed for estimating disturbances and uncertainties in the double closed-loop, and for feedback. In [86], an exact model-based motion estimator was studied to estimate back-EMF and disturbances due to parameter changes. In [87], the Lomberg observer was introduced to estimate the concentrated disturbance in the velocity loop and the current loop, respectively. Compared with the sliding mode observer, the Lomberg observer not only had no chattering, but also had a smaller amount of calculation. In [88], a DPCC based on a compound disturbance observer for dual three-phase PMSM was proposed, the control block diagram is shown as Figure 5. In the composite disturbance observer, a generalized proportional integral observer is used to track time\(-\)varying disturbances. And apply it to the current prediction section to predict the current at time k + 1. Then, combined with the reference current, calculate the reference voltage. And a sliding mode observer was designed to reduce the settling time of the high-order generalized proportional integral observer, so as to avoid the accompanying parameter changes and model during the magnetization process of the permanent magnet decreased control performance due to increased uncertainty.