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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]

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]

To improve the performance of open-winding permanent magnet synchronous motors (OW-PMSMs) with a common dc bus, a novel control method that can simultaneously eliminate parameter mismatch in the d-axis, q-axis, and zero-sequence loop (ZSL) is proposed. First, the parameter mismatches are analyzed. Second, an extended state observer (ESO), which can predict the current in the next instant and the disturbance caused by parameter mismatch, is established. By combining the ESO and the deadbeat predictive current control (DPCC) in the d-axis and q-axis, replacing the sampled current in the DPCC with the predictive current in the ESO, and considering predictive disturbance as a voltage reference feedforward compensation, one-step delay and the disturbance caused by parameter mismatch are addressed. Then, in the ZSL, the predicted zero-sequence disturbance is considered as a compensation for the reference zero-sequence voltage (ZSV). The ZSV is obtained using the zero-voltage vector redistribution strategy in alternate subhexagonal center pulsewidth modulation strategy. The proposed method enhances the robustness of OW-PMSM, against parameter mismatch in the d-axis, q-axis, or ZSL. To verify the effectiveness of the proposed method, simulation and experimental results obtained using the traditional DPCC method and the ESO+DPCC method are presented herein. [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]

To improve the suppression ability of uncertain disturbance of the sliding mode control driving system of the surface-mounted permanent magnet synchronous motor (SPMSM) and to reduce the chattering of the control output, a robust sliding mode control strategy with an improved power reaching law (IPRL) is proposed in this paper. Compared with the traditional fast power reaching law (FPRL), the IPRL incorporates the sum of the power terms of the system state variables into the conventional power terms, and uses hyperbolic tangent saturation function to replace the piecewise function, which can effectively suppress the sliding mode chattering and improve the convergence speed of the system state to the sliding mode surface. Furthermore, the robust sliding mode speed controller and sliding mode current controller of the SPMSM are designed separately with the IPRL, and detailed simulation verification is carried out to reveal the effectiveness of the IPRL. Simulation and experimental results show that compared with the FPRL, the proposed IPRL can reduce the inherent chattering phenomenon in sliding mode control, and the IPRL-based speed and current control strategy can effectively improve the dynamic performance and robustness of the system. [ABSTRACT FROM AUTHOR]