Brain emotional learning based intelligent controller for stepper motor trajectory tracking

Excellent attributes of permanent magnet stepper motor (PMSM) make it prominent in robotic, aerospace, and numerical machine applications. However, the problem of nonlinearity and presence of mechanical configuration changes, particularly in precision reference trajectory tracking, must be put into perspective. In this paper, a novel cognitive strategy based on the emotional learning in limbic system of mammalian’s brain is employed to establish an intelligent controller in order to provide the necessary control actions as to achieve trajectory tracking of the rotor speed in different circumstances. Brain emotional learning based intelligent controller (BELBIC) is a model free controller, independent of model dynamic and variations that occurs in system, can be taken in to account as an outstanding option for the nonlinear applications. Fast response, high accuracy, and the ability of disturbance rejection introduce BELBIC as an eminent controller. To verify these attributes, different test beds have been simulated in Matlab Simulink environment and the performance of BELBIC is investigated. For further illumination, a classic controller called static proportional-integral-derivative (PID) is also applied on the model and then a comprehensive comparison, both in certain and uncertain condition, between the results of the proposed controllers is done. Uncertain situation is provided by applying load torque disturbance and variation in parameters of PMSM. The results of simulations clearly indicate the outstanding ability of BELBIC in speed tracking with high accuracy for the arbitrary reference signals and conspicuous robustness of this controller in presence of uncertainties.