# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### FCS Model Predictive Control of PMSM
#### 3.2.5 Robust Control Strategy  
Some improved methods are proposed for the robustness of predictive control. Reference [141] is based on a disturbance observer, an improved stator flux observer, and an electromagnetic torque observer to obtain accurate parameter information to increase the robustness of the system. An enhanced predictive current control scheme with disturbance estimation and current compensation was proposed in [142]. A sliding mode observer was established to estimate the disturbance and compensate it to the prediction model in real-time. Meanwhile, a new hyperbolic tangent sliding mode approach rate was proposed to effectively suppress chattering. In [143], based on a discrete power system, the switch of the inverter will cause a deviation in the prediction, so the error compensation should be considered in the prediction process. In [144], based on the incremental moving horizon estimator, a direct MPCC with disturbance observer is proposed to improve the robustness of the motor system, and the feasibility of the method was verified by experiments based on a permanent magnet synchronous motor. Reference [145] proposes a model predictive control method based on an autoregressive disturbance estimator, which estimates the disturbance of the motor based on the disturbance observer and returns to the control system in real-time for compensation, effectively suppressing the influence of the disturbance on the torque and speed of the motor.  
On the other hand, for parameter mismatch, reference [146] proposes a solution to the sensitivity of parameter mismatch of a surface-mounted permanent magnet synchronous motor. Figure 10 shows the structure diagram and experimental results of the disturbance controller. By taking into account the different conditions of resistance, inductance, and flux mismatch, the inductance perturbation estimation observer and perturbation controller were built to obtain precise inductance information and apply it to the compensation control system in real-time. A novel current update mechanism for finite set model predictive control was proposed. Reference [147] designed a discrete integral controller by predicting the current error to obtain the inductance information and replace the inaccurate flux linkage parameters, which improved the robustness of MPC while removing the current error under the parameter mismatch. For the robust control of predictive torque flux control, references [148; 149] successively proposed a flux torque predictive controller  
Figure 9: Selected vectors and the divided sectors in the \(\alpha\)–\(\beta\) subspace.  
with parameter robustness to obtain accurate predictive values and improve the control performance of the algorithm. Based on the problems of parameter mismatch, digital delay, and external interference, a predictive current control strategy based on an enhanced extended state observer was proposed in [150].  
In [151], the algorithm for detecting discrete time parameters developed Popov's hyperstability criterion, and determined discrete time model parameters based on adaptive system reference technology were used to achieve strong FCS-MPC control. Reference [152] added an LMS algorithm for web-based management, a new algorithm for system adaptive dynamic model calculation based on measured values. Reference [153] proposes the Adaline neural network algorithm applied to detect inductance in the \(d\)-\(q\) axis. The constant magnetic flow of the motor rotor is combined with the control device to attain decent stable conditions and strong performance. Reference [154]'s data-based recursive calculation method offers real-time power management and a permanent magnet synchronous engine. The data-based strategy does not use physical parameters and has more advantages than all of these WB models. The method is used to evaluate the engine model online and continuously, and the non-linear compensation is weighed using an inverter.