Control Strategy of Synchronous Reluctance Motor Using Empirical Information Brain Emotional Learning Based Intelligent Controller Considering Magnetic Saturation

The synchronous reluctance motor (SynRM) has significant nonlinear characteristics due to the problems of magnetic saturation and cross-coupling and the poor adaptability of the general controller to parameter changes seriously affects the control performance of the motor. In order to solve the above problems, this paper proposed a control system for the SynRM with a brain emotion controller based on empirical information to solve the motor control problem of magnetic saturation. Firstly, the nonlinear mathematical model of the SynRM considering magnetic saturation is established by introducing the magnetic saturation parameter. Secondly, the sensory input function and emotional cue function based on systematic error are given and the vector control system of the SynRM considering magnetic saturation is designed. The influence of the parameters and the learning rate of the brain emotional learning based intelligent controller (EI-BELBIC) on the adjustment range of the controller parameters is studied. Then the SynRM is controlled under different working conditions and the control effect is observed. The results show that the designed vector control system of the SynRM based on EI-BELBIC has strong reliability, accurate control, rapid response, and strong anti-interference ability under magnetic saturation.