Marine electric propulsion is an important topic in the research of modern ships and underwater vehicles. The propulsion motor drives based on model predictive control (MPC) are becoming increasingly popular in marine propulsion systems as an emerging technology. However, the multi-objective optimization in conventional MPC requires cumbersome weighting factor tuning. The relatively large computational cost is also detrimental to the industrial application of MPC. Aiming at reducing the computational complexity of multi-objective optimization without weighting factors, this paper proposes an enhanced ranking-based MPC method for induction motor drives in marine electric power propulsion. The presented control set pre-optimization aims to reduce the computational complexity of enumeration and ranking. Based on the sign of torque prediction deviation, the proposed method avoids enumerating all fundamental voltage vectors. Consequently, the number of candidate elements in the initial control set are reduced to four without excessively excluding feasible solutions. By converting predicted numerical errors into ranking results, the proposed MPC seeks the optimal solution among the candidates through improved ranking evaluation. Considering the situation of simultaneous optimal ranking, the normalization error judgment is developed to further optimize the optimal solution selection process. The simulation and experimental results confirm that the proposed MPC is simple and effective. Without the involvement of tuning the weighting factors, the proposed method achieves good performance.

In this paper, an efficient two-vector-based sequential model predictive control (ETV-SMPC) method is proposed for induction motor drives. It aims to provide an effective scheme for SMPC with extended control set. A fast selection mechanism for the candidate set is presented to generate feasible combinations. The proposed method reduces the computational cost by excluding a considerable portion of the redundant candidates. Due to the flexible sequential evaluation structure, the cost functions for evaluating torque and flux errors are developed in different forms, effectively improving the evaluation performance over that of conventional designs. By analyzing the two-vector control implementation in different scenarios, the torque evaluation stage is designed for the global optimization of torque control. Considering the tracking errors at the switching instant and the end of the control period, the flux error evaluation process is further optimized. In addition, candidates for the flux evaluation stage are adjusted based on the preset torque tolerance. As a result, the proposed ETV-SMPC exhibits better steady-state performance. Additionally, it does not require weighting factors in the cost function and the corresponding tuning work. The experimental results confirm its effectiveness.

Model predictive current control has received a great deal of attention in the field of induction motor drives. However, the most methods select voltage vectors based on enumeration and cost function, so it greatly increases control system complexity. In order to reduce control complexity and further improve steady-state control performance, a predictive current duty cycle control based on the reference voltage (PCDCC-RV) is proposed in this paper. The applied voltage vector is determined based on the location of the synthesized reference voltage, so the complicated enumeration process is cancelled. In addition, the control duty cycle is calculated based on the principle of minimum error between the selected vector and reference voltage. Compared with the traditional method, the proposed method of voltage vector selection and duty cycle calculation are more convenient, and the better control performance is obtained. Finally, simulation results prove its effectiveness.