In this paper, a virtual-flux finite control set model predictive control (MPC) strategy for dual three-phase interior permanent magnet synchronous motors (DTP-IPMSM) is proposed. The technique is based on the conventional predictive current control, but it maps the measured variables into a virtual- flux domain, thus simplifying the prediction stage. The technique uses a flux-based cost function to track the estimated reference flux. The flux tracking cancels out to guarantee an improved current tracking, and thus, a better torque ripple. The proposed technique is validated through simulation of a 100 kW DTP- IPMSM in Matlab/Simulink. Results evidenced a reduced current control error, thus improving the torque tracking up to 38.9 % when compared to the conventional inductance based model predictive control.

This paper proposes an adaptive voltage regulation (VR) flux-weakening (FW) control technique for dual three-phase (DTP) permanent magnet synchronous machine (PMSM) drives. Firstly, a small-signal model is developed to demonstrate the need of gain adaptation in order to maintain the controller bandwidth. Moreover, the small-signal model serves as a tuning tool for the FW proportional-integral (PI) controller. Then, the proposed adaptive controller is implemented and tested on a 100 kW DTP-PMSM drive. Simulation results demonstrate an improved drive performance at high speeds when benchmarked against non-adaptive controllers. Furthermore, the adaptive controller offers an extended speed range.

In this paper, a new method to reduce the steady state torque tracking error of finite control set model predictive torque control for switched reluctance motor drives is proposed. The steady state tracking error is considered as one of the main shortcomings of the conventional Finite Control Set Model Predictive Control (FCS-MPC). This can happen due to parameter uncertainties or when the multiple objectives are achieved by a single function with weighting factors. In the conventional model predictive torque control for SRM, the control action is obtained by a multi-objective cost function designed to track a reference torque while minimizing the phase currents over the prediction horizon. The optimal switching state which minimizes the cost function is selected and applied at each switching instant, which results in the steady state torque tracking error. In this paper, a compensation term is added to the reference torque at each sample instant to minimize the torque tracking error. The compensation term is calculated based on the estimated average torque tracking error in the previous sample times. Simulations on a three phase, 12/8, 2.3 kW SRM show promising results with the proposed method as compared to the conventional FCS-MPC.

In this paper, a virtual-flux finite control set model predictive control (FCS-MPC) strategy of switched reluctance motor (SRM) drives is proposed. This technique uses a flux linkage-tracking algorithm to indirectly control the phase current. The algorithm is based on an estimated virtual flux obtained from the static characteristics of the machine. A cost function is used to evaluate the switching state that produces the minimum error. A state graph for switching states limitation is also proposed to reduce the number of commutations and computational burden. Simulation results evidence the enhanced performance of the proposed technique with respect to hysteresis control for current tracking using two different current shaping techniques: torque sharing function (TSF) and radial force shaping (RFS).

This paper proposes a simplified convex optimization-based sensorless scheme for interior permanent magnet synchronous motor (IPMSM)drives. The computational burden of the existing convex optimization-based method is significantly reduced by a single variable cost function, which is based on the machine voltage equations in the stationary reference frame. With less computation, the proposed method provides good performance characteristics similar to the existing one, e.g., dynamic speed response and smooth transition between the low and high-speed ranges. The convergence capability of the cost function is also confirmed by a convexity analysis. The feasibility of the control technique is experimentally validated in a test bench, demonstrating the accuracy of the technique and its reduced computational burden.