Novel SMA BASED Elmanspiking neural network modelled fuzzy PI controller for speed-torque regulation of PMSM

In the current industrial scenario, permanent magnet synchronous motors are widely employed for drive based applications and many other robotics and machine tool applications. With a simple structure and high torque-to-inertia ratio, PMSM are able to be operated even in medical industry and laboratory experimentation set ups. The main limitation of PMSM is the presence of inherent coupled flux and torque which makes it very difficult to control. This paper focuses on fuzzy based PI controllers along with novel neural based controller for speed control of PMSM. A novel slime mould algorithm based Elman spiking neural network (ESNN) model hybridized with fuzzy inference proportional-integral controller is designed in this paper to regulate the speed and torque of permanent magnet synchronous motor drive. Due to the existence of randomness in the proposed soft computing controller, it is tested for its validity and suitability by performing statistical analysis and is observed to be valid to act as a controller model for PMSM drive mechanism. In this paper, this soft computing controller possess randomness during first phase of weight update and during optimal gain value determination Simulation process for the designed new soft computing controller was done in MATLAB.