Adaptive intelligent speed control of switched reluctance motors with torque ripple reduction

Switched reluctance (SR) motors have a wide range of applications in industries, mainly due to the special properties of this motor. However, because of its dynamical nonlinearities, its control is complex. This paper presents an adaptive intelligent control based on the Lyapunov stability theory to control the speed of SR motors with good accuracies and performances. The proposed controller is composed of a speed controller and a torque controller. The main parts of the speed controller are two-fold: (a) the optimal controller, which is based on the Hamilton–Jacobi–Bellman theory and (b) the intelligent controller, which is an adaptive fuzzy controller. The main features of the proposed speed controller are: (1) its independence of the exact parameters of the SR motor such as the inertia of rotor, the viscous friction and the load torque and (2) the robustness to inaccuracies and disturbances. Moreover, the torque ripple reduction is achieved by employing a neural network for torque estimation. The simulation results show good performance of the proposed controller in speed controlling and torque ripple reduction.