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One of the main obstacles to the robust optimization for permanent magnet machines is the high computational burden, which is mainly caused by the robustness evaluation process considering the manufacturing uncertainties. In this paper, a sequential sampling Kriging model is adopted to estimate the design robustness, and the problem of high-dimensional variables for the surrogate model due to uncertainties is avoided through adopting the worst-case based approach. To further reduce the number of finite element analyses (FEA) required for constructing a metamodel, a multi-points sequential sampling process with a two-step optimization oriented surrogate model updating algorithm is proposed. Compared with the FEA directly based robust optimization in previous work, similar Pareto Fronts are achieved by adopting the meta-model proposed algorithm but the overall run time reduced by 75%. In the end, an optimization problem for a 1.2 kW motor is considered by applying the proposed algorithm, and two prototypes with deliberately designed tolerances, imitating the worst-case scenarios in the real world, are manufactured. The worst-case torque ripple is reduced from 7.6% to 4.8% after optimizing, and this verifies the efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Conventionally, the small mean and variance of peak-to-peak cogging torque of permanent magnet (PM) machines considering manufacturing uncertainties can be achieved by a robust design or by reducing the uncertainties range. However, the consequent compromise of other design objectives or the increase in the manufacturing costs are frequently inevitable. In this paper, the combination sequence of the uncertainties is highlighted and implemented to achieve a stable performance even for a non-robust design without increasing the cost too much. A PM assembling approach is proposed to mitigate the influences of PM uncertainties on cogging torque by means of combining the uncertainties in a particular sequence, where the effects of uncertainties would counteract each other. Both the singular type of PM uncertainties and the combined ones are considered in the assembling approach. Furthermore, the proposed approach is verified by comparing the cogging torque performance with PM randomly assembled models, where several hundreds of models featuring different uncertainties are calculated through the finite element method. The proposed approach is discussed, particularly for the application of mass production and a small amount of prototypes. Two prototypes with different PM assembling sequences are fabricated and further verify the effectiveness of the proposed PM assembling approach. [ABSTRACT FROM AUTHOR]

To improve the torque accuracy for open-end winding permanent magnet synchronous motor (OEW-PMSM) with common dc bus, it is necessary to eliminate the zero-sequence torque (ZST) caused by zero-sequence current (ZSC) and the third back electromotive force. But the third flux linkage mismatch can enlarge ZSC which causes torque ripple. To deal with the above problem, firstly, a q-axis current injection method combined with deadbeat predictive current control (DPCC) is adopted to inject an extra current into the reference q-axis current. The extra torque generated by this injected current can compensate for ZST. Secondly, to ensure the accuracy of the q-axis injected current, a parameter identification method based on recursive least square (RLS) algorithm with forgetting factor is presented to identify the third flux linkage accurately. The identification value can reduce ZSC and torque ripple under different working conditions. And the accurate q-axis injected current can be obtained to counteract ZST and output accurate torque. Finally, three methods are comparatively analyzed in this paper, namely the traditional DPCC method, the DPCC method with q-axis current injection and the proposed DPCC method based on RLS with forgetting factor. The simulation and experimental results verify the effectiveness of the proposed DPCC method. [ABSTRACT FROM AUTHOR]

Torque ripple reduction has become an active research area for permanent magnet synchronous machine (PMSM) drives. This paper presents a novel torque ripple reduction method based on harmonic current control. In the proposed method, the optimal harmonic current solution based on the torque ripple model with minimum stator resistive loss is derived in the rotor reference frame (RRF). The optimal harmonic currents are injected by the proposed harmonic current controller (HCC), wherein the magnitude of harmonic currents are defined as controller variables. To estimate the harmonic currents, the proposed method also utilizes the least mean square (LMS) based adaptive filter (AF), wherein the coefficients are defined as feedback to the proposed HCC. The proposed methodology is demonstrated using both simulations and experiments and is verified to reduce torque ripples of PMSM drives over a wide range of speeds. [ABSTRACT FROM AUTHOR]

Deadbeat predictive current control (DPCC) can predict motor behavior based on SPMSM model. However, during the operation of motor system, motor parameters (such as stator inductance and flux linkage) vary frequently according to different working conditions, which may lead to controller parameter mismatch, causing current harmonic content to increase and efficiency to decrease. In order to solve these problems caused by parameter variation, first, this paper proposes a current prediction error model by considering uncertainties of model parameters. Second, stator inductance and flux linkage are decoupled based on current prediction error model, which can reduce the interaction between parameters. Finally, the Kalman Filter (KF) algorithm is presented to filter the decoupled parameters. It is shown that the stator inductance and flux linkage can be identified accurately and the complexity of computation can be simplified. The traditional DPCC method, Extended Kalman Filter (EKF) based DPCC method and the proposed DPCC method are comparatively analyzed in this paper. Simulation and experiment indicate that the proposed parameter decoupling identification method can effectively reduce current harmonic content, current fluctuation and current tracking errors caused by parameter mismatch. [ABSTRACT FROM AUTHOR]

This article discusses a method to consider the effects of the manufacturing uncertainties on the additional cogging torque harmonics. With this method, the sensitivity analysis can be incorporated into the design stage instead of just checking it afterwards. With the authors’ previous work of the worst-uncertain-combination-analysis (WUCA) method, several design parameters and their effects on the additional cogging torque harmonics under uncertainties are discussed. It is demonstrated that a design with inherent robustness can be obtained through the proper selection of pole-arc coefficient and slot-opening width. In addition, a sensitivity index is proposed to estimate the effects of parameter uncertainties on torque harmonics. Some suggestions on improving the design robustness are consequently provided from the aspects of tolerances control and parameter design. The deductions are verified through finite element analysis (FEA) on different pole/slot configurations. [ABSTRACT FROM AUTHOR]

One of the main obstacles to applying the robust optimization for the permanent magnet motors is the high computational burden, which is mainly caused by the robustness evaluation considering the manufacturing uncertainties. In this article, efforts are made to speed up the process of robust optimization, from the aspects of reducing the computational cost, and avoiding the computing resources being wasted. Firstly, several widely used methods, aiming to identify the worst-case performance under uncertainties, are examined, and compared. And the efficient worst-uncertain-combination-analysis (WUCA) method is consequently adopted to significantly reduce the computational cost of robustness evaluation. From another aspect, the parameter rounding (PR) operation is inevitable due to the constraint of manufacturing accuracy, but its adverse effects on the optimization are often ignored. It is found that parts of the computing resources might be wasted during the optimization. A new PR during the calculation (PRDC) method is proposed, and incorporated into the process of robust optimization. Compared with the conventional one, similar results can be achieved with the computational time reduced by approximately $\text{11}\%$ in the PRDC modified robust optimization. [ABSTRACT FROM AUTHOR]

Permanent-magnet (PM) motors have been widely used in industrial applications and automobiles. However, owing to the design tolerances, manufacturing uncertainties, and material inconsistency, deviations from the ideal motor inevitably occur with parasitic effects, such as additional cogging torque and vibration. One of the most significant obstacles for evaluating these uncertainties is the large computational burden caused by the countless uncertain combinations, which must be computed via the finite-element method (FEM). Herein, a worst-uncertain-combination-analyze (WUCA) method is proposed to significantly reduce the computational cost. A widely used analytical method is modified to identify the origin of additional cogging-torque harmonics caused by uncertainties. Different types of uncertainties for the surface-mounted PM machine and interior PM machine can be analyzed simultaneously. With the WUCA method, the worst-case combinations can be confirmed theoretically; thus, FEM calculations for only a few combinations of uncertainties are required, rather than thousands. Compared with the widely adopted design of experiments based uncertain combining methods, the worst-case cogging torque obtained from the WUCA is higher. FEM verification of different pole/slot configurations revealed that the WUCA method was a general and effective method for quickly estimating the worst-case cogging torque under manufacturing uncertainties. [ABSTRACT FROM AUTHOR]

Conventional finite-control set model predictive current control predicts the motor behavior for every switching state and selects the voltage vector that can minimize the cost functions as an optimum voltage vector. To get rid of the model parameter dependency, a finite-control set nonparametric predictive current control (NPCC) was proposed before, which can predict future current behavior using the measured data instead of initial model parameters. However, one of the main issues that are limiting the current and torque ripple performance is a stagnant current update mechanism. To overcome this issue, this article proposes a novel current update mechanism that based on two most recent current variations to reconstruct the permanent magnet synchronous machine model. The proposed nonparametric predictive current control based on the current update mechanism can effectively reduce the torque ripple and enhance current performance in contrast to NPCC. Experiment results are demonstrated to verify the effectiveness of the proposed method under different operating conditions. [ABSTRACT FROM AUTHOR]

In order to suppress the torque ripple of an open-end winding permanent-magnet synchronous machine (OEW-PMSM), a q-axis current injection method is typically employed but the disturbances caused by the third flux linkage parameter mismatch can augment the torque ripple. To solve this problem, in this paper, first, a zero-sequence current (ZSC) and zero-sequence back-EMF observer (ZCBO) are proposed based on an adaptive sliding mode control, which is able to simultaneously estimate ZSC and zero-sequence back EMF under varying conditions. Based on this ZCBO, the estimated ZSC and zero-sequence back EMF could compensate for the one-step control delay and suppress the aforementioned disturbances. Second, although central hexagon modulation is employed in the predictive current control (PCC) scheme, zero-sequence voltage (ZSV) still exists in the zero-sequence path due to the dead time of the inverter, affecting the predictive accuracy of the ZCBO. To accurately establish the ZCBO, the ZSV caused by the dead time of the inverter is considered in the ZCBO. Finally, a comparative study of two types of methods is presented, and simulations established by MATLAB software and experimental confirmations are carried out to verify the effectiveness of the proposed PCC scheme in this paper. [ABSTRACT FROM AUTHOR]

Model predictive current control (MPCC) can predict future motor behavior according to a motor model. In practice, however, motor parameters will vary at run time, and the parameter mismatch disturbances caused by the variation in motor parameters will deteriorate the MPCC performance. To suppress the parameter mismatch disturbances effectively, this paper proposes a modified MPCC with a current variation update mechanism. In contrast with the traditional current prediction equation that contains crude model parameters, the modified current prediction equation contains only measured information, taking advantage of the proposed current variation update mechanism, which can update the modified prediction equation within each sampling period. A simulation established by MATLAB software indicates that the proposed method can effectively suppress the parameter mismatch disturbances. Experiments are carried out to verify the correctness of the proposed method. [ABSTRACT FROM AUTHOR]

In an open-end winding permanent magnet synchronous machines drive with single dc voltage source, zero-sequence current (ZSC) can lead to a high current stress of power modules and loss of motor. Therefore, some modulation strategies have been employed to alleviate ZSC by suppressing zero-sequence voltage (ZSV). However, ZSC still exists in the system because ZSV can also be generated by other nonlinear factors of inverter such as the dead time of system. In addition, zero-sequence back electromotive force in the zero-sequence path can also enlarge torque ripple and ZSC. In order to deal with above problems, first, this paper proposes a full-order adaptive zero-sequence observer to estimate future ZSC and ZSV, which are able to compensate one-step control delay. Second, to achieve the maximum voltage dc bus utilization, this paper proposes a novel deadbeat predictive current control (DPCC) scheme with alternate sub-hexagonal center pulsewidth modulation strategy to suppress ZSC and torque ripple simultaneously. Finally, this paper presents a comparative study of two types of methods, namely traditional DPCC scheme and the proposed DPCC scheme. Simulation and experimental results are demonstrated to verify the effectiveness of the proposed DPCC scheme. [ABSTRACT FROM AUTHOR]

The efficiency performance of multi-motor-driven system highly depends on the power management. Three aspects of contribution have been made in this study. 1) A predictive power management for a DMPS is developed. To improve the performance of the predictive power management, an adaptive velocity predictor is proposed and the coefficients of proposed predictor can update its parameters according to identified driving patterns. Simulation results show that the new velocity predictor have best prediction performance compared with traditional predictors. 2) A neural network based power management is proposed. According to the optimization results of dynamic programming, radial-basis-function neural network is trained. The input dimensions and the number of hidden layer neurons of the neural network are optimized. 3) The performance of proposed control strategies are compared with three different drive cycles including MANHATTAN cycle, Japanese 1015 cycle, and UDDSHDV cycle. Simulation results indicate that compared with original control strategy, the predictive control strategy and neural network based control strategy show better efficiency performance. The neural network based strategy is verified by hardware-in-loop experiment and experiment results indicate that the control performance in real hardware shows similar property with simulation results. [ABSTRACT FROM AUTHOR]