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Preferences in multi-objective evolutionary optimisation of electric motor speed control with hardware in the loop

机译:具有硬件回路的电动机速度控制的多目标进化优化中的偏好

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This paper presents the design of a robust Pareto-optimal controller with the designer preference articulation for a Permanent Magnet Synchronous Motor (PMSM). An evolutionary multi-objective optimisation (EMOO) algorithm is used to tune the proportional integral (PI) speed regulator in the Direct Torque Control drive system. Approximation of the Pareto front with hardware in the loop (HiL) is chosen as an alternative to the time-consuming software simulation studies. Thanks to this approach problems of un-modelled plant dynamics are alleviated and additional manual tuning on-line is not required. The weak point of the HiL approach is caused by disruptive presence of noise which affects the performance of EMOO. This influence is strongly problem-dependent; therefore no generalized results have yet been presented in the literature. In this paper the robustness features of the proposed design approach are verified using the state-of-the-art multi-objective evolutionary Non-dominated Sorting Genetic Algorithm (NSGA-Il; Deb, 2001 [1]). The on-line optimised motor drive speed controller is shown to be effective, possessing good dynamic characteristics, demonstrating applicability of the a priori preference articulation technique to the controller design. The final Pareto-optimal solution is selected according to the designer's preference articulation before the Pareto front is approximated by EMOO.
机译:本文提出了一种鲁棒的帕累托最优控制器的设计,该控制器具有设计者对永磁同步电动机(PMSM)的偏好表达。进化多目标优化(EMOO)算法用于调整直接转矩控制驱动系统中的比例积分(PI)速度调节器。选择帕累托前沿与回路中的硬件(HiL)进行近似,以替代耗时的软件模拟研究。由于采用了这种方法,可以缓解未建模的工厂动态问题,并且无需进行其他在线手动调整。 HiL方法的弱点是由噪声的破坏性存在引起的,这会影响EMOO的性能。这种影响很大程度上取决于问题。因此,在文献中尚无广义的结果。本文使用最新的多目标进化非支配排序遗传算法(NSGA-11,Deb,2001 [1])验证了所提出设计方法的鲁棒性特征。在线优化的电动机驱动速度控制器被证明是有效的,具有良好的动态特性,证明了先验优先铰接技术在控制器设计中的适用性。最终的帕累托最优解是根据设计者的偏好进行选择的,然后再通过EMOO对帕累托前沿进行近似。

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