Investigation and analysis of iterative learning-based current control algorithm for switched reluctance motor applications

In this paper, a novel current tracking strategy for switched reluctance motor (SRM) is proposed, analyzed and verified. The relationship between pulse-width modulation (PWM) duty ratio and current variation in SRM over a cycle is investigated. The results demonstrate that an analytical resolution for calculating the duty ratio to achieve accurate current tracking is not possible because of the nonlinear inductance profile of SRM. Consequently, an iterative learning current control method is proposed, that can calculate the optimum duty ratio for tracking the reference current through periodical learning at different rotor positions in real-time. The learning gain for this proposed iterative learning-based current control algorithm is investigated and its upper limit is calculated by considering system convergence. In addition, an improved average current tracking strategy is presented in this paper. The numerical analysis performed in this paper clearly demonstrates the effectiveness of the proposed current control method for different speed and loading conditions.