# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 1 Introduction  
Permanent magnet synchronous motors (PMSMs) have established widespread attention and research because of their compact structure, high torque-to-current ratio, high power density, good fault tolerance, and high work efficiency [1; 2; 3; 4; 5; 6]. At present, permanent magnet synchronous motors are widely used in many industrial fields, such as for ships, engineering machinery, aerospace engines, and intelligent robots [7; 8; 9; 10; 11]. At the same time, with the development of electric vehicles, PMSMs are widely used in the field of vehicles due to their flexible control methods and high reliability, and are gradually becoming mainstream vehicle drive motors [12; 13; 14; 15; 16]. The classic control methods of the PMSM drive system include vector control [17; 18; 19] and direct torque control [20; 21; 22; 23; 24]. Generally, the PI controller (proportional-integral) of vector control involves more intermediate variables in the design process of parameter tuning. However, DTC control has a large torque ripple and causes high-frequency noise. Therefore, with the development of modern control technology, position sensorless control [25; 26; 27; 28; 29; 30], sliding mode control [31; 32; 33; 34; 35], fault-tolerant control [36; 37; 38; 39; 40; 41], and predictive control [42; 43; 44; 45; 46] have been studied. Since the input signals of power electronic devices are discrete, discrete-based digital control technology has developed rapidly. At the same time, with the rapid development of the field of digital signal processing, the computing power of chips has been greatly enhanced, so predictive control has attracted much attention. Predictive control was proposed and used as early as the 1980s. According to different predictive models and optimization algorithms, it is classified according to different characteristics of predictive methods in the field of electric drive. Predictive control can be divided into DPC [47; 48; 49; 50; 51; 52], hysteresis predictive control [53; 54; 55], trajectory predictive control [56; 57; 58], and MPC. Table 1 details the characteristics of each model. Among them, MPC can be divided into finite-control-set model predictive control and continuous-control-set model predictive control. Predictive control has received more attention and has been extensively studied, which can improve the dynamic performance and stability of permanent magnet synchronous motors to adapt to complex and harsh application environments. With the development of modern control technology, power electronics technology, and finite element simulation technology [59; 60; 61], predictive control has been extensively studied. New predictive control strategies have been proposed one after another, meeting higher prediction accuracy, higher reliability, and better dynamic response performance. In predictive control, DPC and MPC are most suitable for PMSM, and they are also the two most widely studied at present. Deadbeat predictive control has the advantages of decent dynamic performance, high control precision, and low calculation amount. For deadbeat current predictive control, it calculates the voltage based on a discretized mathematical model and converts it into a switching signal through pulse width modulation technology. Model predictive control, as a modern control theory, is based on discrete dynamic systems and optimizes the control variables in the future according to the predictive model of the system to achieve the optimal control effect. In MPC, the controller can compute the best control input according to the current state and the predicted value of the future state, and then revise the prediction model and update the control strategy through feedback. MPC can jointly control multiple control variables, and can deal with complex control problems including nonlinear, time-varying, and multivariable problems. Therefore, MPC is perfectly suitable for PMSM strongly coupled nonlinear systems.