Finite Control Set Model Predictive Control (FCS-MPC) calculates torque and flux tracking errors via a cost function that is used for selecting the optimal vector. Compared with Field Oriented Control(FOC), FCS-MPC has the merit of a faster dynamic performance because it eliminates both pulse-width modulation (PWM) and inner proportion-integration (PI) controllers. However, the weighting factor for modifying torque and flux terms must be tuned in accordance with varying operating conditions; this is an area in which further research is needed. In this paper, a Parallel Predictive Torque Control (PPTC) with adaptive constraints is proposed as a solution for this problem. The PPTC method optimizes torque and flux terms simultaneously, and switching-state candidates are then selected in an adaptive mechanism. The key feature is that torque and flux tracking errors are constrained within the optimized boundaries. The proposed PPTC is compared with the state-of-the-art PTC method. Both simulation and experimental results confirm that the proposed method, which has no weighting factor, achieves an even better dynamic performance and robustness than the conventional PTC.

The use of artificial neural networks (ANNs) for the selection of weighting factors in cost function of the finite-set model-predictive control (FS-MPC) algorithm can speed up selection without imposing additional computational burden to the algorithm and ensure that optimum weights are selected for the specific application. In this article, the ANN-based design process of the weighting factors is used for predictive torque control (PTC) in a motor drive. In the design process, the weighting factors in the cost function and the reference flux value are obtained using different fitness functions. The results show that different operating conditions of the drive will have new optimum parameters of the cost function; therefore, sweeping parameters like load torque or reference speed can optimize the PTC for the whole operating range of the drive. A good match of the performance metrics predicted by the ANN and the simulation model is also observed. The experiments demonstrate that the selected cost function parameters can provide a fast drive start and good performance during different loading conditions and also in reversing of the drive.

Although the robustness is enhanced essentially by the model-free predictive control, the accuracy of its data-driven model is directly affected by the unnecessary current harmonics, such as the harmonics caused by the control strategy itself. To reduce the influences of these harmonics, a continuous-control-set model-free predictive fundamental current control (MF-PFCC) strategy is proposed in this article for the permanent magnet synchronous motor driving system. Harmonics due to the controller including side frequency effect and frequency coupling effect are analyzed in principle, and a frequency-converting double second-order generalized integrator structure is designed to exclude the fundamental terms from the sampled current to cut the coupling path of the effects and to improve the accuracy of the data-driven model in the MF-PFCC without any time-variable physical parameters. The analyses of the effects and the effectiveness of the proposed method are verified by experimental and supplementary simulation results, as well as advantages including the reduced influence of effects and enhanced robustness with improved stator current quality.

This paper proposes a robust model reference adaptive system (MRAS) estimator incorporating online parameter identification algorithm for parallel predictive torque control (PPTC) scheme. In contrast to conventional predictive torque control (PTC), the proposed PPTC is designed to facilitate the determination of weighting factor that modifies the relative importance of the control objectives, by simultaneously evaluating the torque and flux tracking error terms. The weighting factor is fine-tuned via an adaptive selecting mechanism, to obtain the attractive feature of reduced torque and current tracking errors. Besides, an encoderless MRAS-based estimator is employed for rotor speed and stator flux estimation to reduce the hardware complexity. However, this proposed encoderless PPTC algorithm still suffers from weak robustness against inevitable disturbances caused by parameter variations, which is an important issue to be further investigated. In this paper, an encoderless MRAS estimator combined with online parameter identification algorithm is proposed as an effective solution to accurately estimate the rotor speed and predicted stator flux-linkage, that improves robustness against mismatched parameters for the proposed encoderless PPTC scheme. The feasibility of the proposed algorithm is verified through the obtained experimental measurements, that achieves precise speed tracking capability as well as reduced torque and current tracking errors.

Finite control set model predictive control (FCS-MPC) has gained increasing popularity as an emerging control strategy for electrical drive systems. However, it is still a challenging task to optimize weighting parameters, as multiple objectives are involved in the customized cost function. A cooperative decision-making approach for FCS-MPC is proposed in this article, to solve the optimization problems with manifold control objectives. By splitting the cost function, the optimization problem underlying multiobjective FCS-MPC is separated into a series of decomposed optimization problems. By doing so, the dimension of the decomposed problem is reduced to one. To collect the information for decision-making, an efficient sorting algorithm is applied for each control objective. The theory behind the cooperative decision-making approach is comprehensively analyzed, to validate both the effectiveness and efficiency of the proposed scheme. More specifically, the highlight is the adaptive mechanism on the number of desired candidates, to obtain a decent performance for torque and flux. The candidate that minimizes the switching frequency is selected as the optimal. The proposed scheme is experimentally verified and compared with the existing FCS-MPC without weighting parameters.

Three-level neutral-point-clamped (3L-NPC) power converters are necessary interfaces to form micro-energy systems. Naturally, designing a suitable control scheme, featuring superior dynamics, strong robustness, and simple structure, is a promising solution to guarantee more efficient operation of the converter. This article proposes a robust high-quality current control strategy for the 3L-NPC power converter in the stationary $\alpha \beta$ frame. A super-twisting algorithm coupled with a Luenberger observer current controller is proposed to deal with the poor sinusoidal current tracking issue due to the existing inductance/grid frequency deviations and the disturbance of the sinusoidal dynamic nature. Additionally, an extended sliding mode disturbance observer-based proportional control is built to dramatically enhance the voltage regulation performance, in the case of capacitance deviations and unknown dc-loads. Experimental data confirm the effectiveness of the proposed solution outperforms the conventional proportional-resonant/-integral control in terms of accurate tracking current/voltage, antidisturbance, and grid current total harmonic distortion.