Electrically excited synchronous motors (EESMs) without installing slip rings and brushes are drawing increasing attention in the electric vehicle (EV) propulsion systems. To improve the control performance of the EV EESMs with uncertain mutual inductance, which works under low control frequency (LCF), this paper proposes a novel segmented-prediction-based finite control set model predictive current control (FCS-MPCC) strategy. First, a sliding mode (SM) observer is constructed to identify the mutual inductance, with its stability and robustness against parameter mismatch analyzed. By using the estimated mutual inductance, the accurate EESM model used for FCS-MPCC is established, Second, the segmented prediction algorithms are developed to reduce the prediction errors caused by local linearization in the LPF situations. Finally, the proposed mutual inductance identification and high-performance control techniques are verified by experiment, which is conducted on a 580-W EESM drive system.

In the application of coal mining belt conveyors, dual-motor drives based on permanent magnet motors (PMM) are gaining increasing attention now. To achieve high-performance coordination control of the two motors, this paper proposes a finite control set model predictive speed control (FCS-MPSC) method to improve the dynamics and speed tracking performance of the motors. First, the features of the dual-motor drives used in conveyor belts are analyzed. On this basis, the requirements of the control strategies are illustrated. Second, a master-slave control strategy is developed after treating the PMMs at the tail end and head end as the master motor and slave motor, respectively. Third, the FCS-MPSC method is developed for both master and slave motors by using new predicting model. In this process, the issue that the speed property is not directly related to the manipulated variables are tackled. Moreover, in order to further improve the dynamics of the slave motor, a speed reference compensation strategy is proposed. Finally, the proposed FCS-MPSC method is validated through comparative simulation results.

This paper presents a load change assessment-based feedforward compensation method for finite control set model predictive current control (FCS-MPCC) in permanent magnet synchronous motors (PMSMs). The objective is to address the adverse effects of load disturbances on FCS-MPCC, which can lead to deteriorated control performance and system instability. To mitigate these effects, a novel feedforward compensation mechanism is proposed by integrating a load change assessment mechanism within the FCS-MPCC framework. The mechanism enables real-time estimation of load changes by accurately capturing their rate and direction. A sliding mode torque observer (SMTO) is developed to ensure accurate load estimation, characterized by fast response and strong robustness. The stability of the SMTO is analyzed using a Lyapunov function. Furthermore, a technique is proposed to generate feedforward compensation values based on the load change assessment, specifically applied to the q-axis reference current. Comparative simulation results verify the effectiveness of the proposed strategies.