## 1 Introduction  
AC permanent magnet synchronous motors are widely used in the field of new energy vehicles due to their high density, high efficiency, and good dynamic performance, while their main speed control methods are vector control (VC) [1] and direct torque control (DTC) [2]. VC mainly realizes the decoupling of the current through a simple coordinate transformation to achieve the control of the torque and the magnetic chain, but its dynamic response is slower and it relies on the prediction model too much. DTC is intended to achieve torque and chain control directly through the hysteresis loop comparator [3] and switching vector table [4], and compared with VC, it has a fast dynamic response, simple structure, and less dependence on the motor parameters, with no need to use a pulse-width modulator, but it has larger torque and chain pulsations [5]. With the advancement of microprocessor technology, model predictive control (MPC) [6] is gradually becoming an alternative scheme for motor drive control due to its simple structure, fast dynamic response and the possibility of multiple target control. Among the many control schemes of MPC, the finite control set model predictive control (FCS-MPC) [7] can solve the optimization problem online according to the characteristics of the inverter, the switching state can be discrete, and because its algorithmic complexity is low and the control mode is flexible, it has become a research hotspot in the field of PMSM control at present. The basic steps of FCS-MPC [8] are as follows: 1) use a discrete model of the system to calculate and predict the predicted values of all the control variables of the system; (2) evaluate each switching state of the inverter based on the cost function [9]; and (3) determine the voltage vectors that make the cost function optimal to be applied to the object under control. In motor drive systems, FCS-MPC can be subdivided into model predictive torque control (MPTC) [10] and model predictive current control (MPCC) [11]. MPCC uses the current as the control objective, and the value function contains only one item of current [12], which does not require a weight coefficient design. On the other hand, MPTC takes the electromagnetic torque and stator magnetic chain as the control objective, and the value function also contains the magnetic chain and torque, so it needs to design the weight coefficients, and this paper mainly focuses on the MPTC method. Although the traditional MPTC has superior control performance, it also has the following problems. (1) Its prediction model is more dependent on the parameters [13], and the mismatch of the model parameters will lead to the deviation of the predicted values, which affects the control performance. (2) All eight feasible voltage vectors need to be substituted into the cost function to perform the traversal optimization computation in each sampling cycle [14], which is an onerous computational burden limiting the application of it in the PMTC. (3) Due to its multi-objective control advantage, the constructed cost function contains multiple control terms [15], and the weighting coefficients need to be introduced due to the different magnitudes of controlled quantities [16]. (4) Due to its multi-objective control advantage, the constructed cost function contains multiple control terms [17], while the weighting coefficients need to be introduced due to the different magnitudes of the controlled quantities [18], and a large number of simulations and experimental explorations are required in order to select the appropriate weighting coefficients. In response to the above problems, the literature [19] uses adaptive control to reduce the dependence of the look on the system parameters, but the calculation is cumbersome and the hardware requirements are high. [PERSON].