## 1 Introduction  
In recent years, the synchronous reluctance motor (SynRM) has become a hot topic in motor research due to its low cost, high reliability, high efficiency, low moment of inertia, and safe operation [1, 2, 3, 4]. Although the SynRM has many advantages, the magnetic field is obviously nonlinear and the problems of saturation and cross-coupling are severe due to the characteristics of the body structure [5, 6], which make the traditional universal controller unable to quickly follow changes in the system and seriously affect the transient response and accurate control of the motor. In order to maintain accurate control of a SynRM, the controller needs to adapt to changes in system parameters [7, 8, 9]. Therefore, a variety of intelligent control algorithms are primarily used in the control of SynRMs. For reference [10], based on the known information and reliability of SynRMs, the minimum means square error model with weight was established and corresponding weights were set for different objective functions. The parameters of the inductance model are optimized by using an improved harmonic search optimization algorithm to improve the vector control performance of SynRMs. The literature [11] tracks the flux of maximum torque per ampere (MTPA) operation through virtual signal injection and trains flux diagrams online with data using a self-learning control (SLC) scheme. Then MTPA or maximum torque per voltage (MTPV) control can be realized quickly and accurately depending on the optimal flux amplitude of fast dynamic response after training. The literature [12] has proposed using the natural heuristic algorithm to search for the best parameter of PI and compared the response time and control performance of PI coefficients with the cuckoo search algorithm, whale optimization algorithm, and bat search algorithm, respectively. The research shows that motor speed can be followed quickly and accurately. Using the above algorithms can improve the adaptability and robustness of the control system of SynRMs. However, due to the limited computing power of motor drivers, artificial intelligence algorithms are too complex to be implemented in the system. Moreover, the simplified structure of the applied algorithm sacrifices some control performance in order to reduce the amount of computation to improve computing speed. The theory of brain emotion control was first proposed by Iranian scholar [PERSON] in 2000 [13]. [PERSON] applied a brain emotional learning-based intelligent controller (BELBIC) to control a motor drive for the first time in the literature [14], proving that the response to parameter changes by a BELBIC is fast and stable and it is easier to implement in microprocessors compared with other intelligent algorithms. In the literature [15], a BELBIC is used to maintain speed and flux control of induction motors, which improves the adaptability of the control system to motor parameter changes and control performance. Researchers [16] applied a BELBIC to speed tracking control of hybrid stepper motors (HSMs) and experiments show that the BELBIC has better results in dealing with nonlinear and unknown interference through self-learning and self-adjustment. In this article, a vector control system for SynRMs, based on the EI-BELBIC and addressing magnetic saturation, is proposed to solve the problem of nonlinear motor control under magnetic saturation conditions. The nonlinear mathematical model of SynRMs is established by introducing the magnetic saturation parameter. The sensory input function and emotional cue function based on systematic error are given. Based on the above research, the vector control system of a SynRM, based on the EI-BELBIC, is designed and the influence of the changes to the parameters and the learning rate of the brain emotion controller's adjustment range of the controller parameters is studied to address magnetic saturation. The rest of the paper is organized as follows. In Section 2, the motor model and the brain emotional limbic system calculation model are presented.