Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification
Finite-Control-Set Model Predictive Control for a Permanent Magnet Synchronous Motor Application with Online Least Squares System Identification
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DOI：
10.1109/precede.2019.8753313
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发表时间：
2019-05
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影响因子：
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通讯作者：
Sören Hanke;Sebastian Peitz;Oliver Wallscheid;J. Böcker;M. Dellnitz
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作者：
Sören Hanke;Sebastian Peitz;Oliver Wallscheid;J. Böcker;M. Dellnitz
与电驱动领域中的经典控制方法（如磁场定向控制（FOC））相比，模型预测控制（MPC）方法能够提供更高的控制性能。这是指在多变量控制的情况下，更短的建立时间、更低的过冲和更好的控制变量解耦。然而，这只能在所使用的预测模型足够好地覆盖设备的实际行为的情况下实现。在模型偏差的情况下，使用MPC的性能仍然低于其潜力。这会导致电流涟漪增加或稳态设定点偏差。为了实现高的控制性能，因此有必要使模型适应真实的设备行为。当使用在线系统识别时，不太精确的模型足以用于驱动系统的调试。本文提出了一种将有限控制集预测控制（FCS-MPC）与系统辨识相结合的方法。该方法不需要高频信号注入，而是使用FCS-MPC已经需要的测量值。基于最小二乘法的识别实验室测试台架上的评估表明，模型的精度，从而控制性能可以提高预测模型的在线更新。
In comparison to classical control approaches in the field of electrical drives like the field-oriented control (FOC), model predictive control (MPC) approaches are able to provide a higher control performance. This refers to shorter settling times, lower overshoots, and a better decoupling of control variables in case of multi-variable controls. However, this can only be achieved if the used prediction model covers the actual behavior of the plant sufficiently well. In case of model deviations, the performance utilizing MPC remains below its potential. This results in effects like increased current ripple or steady state setpoint deviations. In order to achieve a high control performance, it is therefore necessary to adapt the model to the real plant behavior. When using an online system identification, a less accurate model is sufficient for commissioning of the drive system. In this paper, the combination of a finite-control-set MPC (FCS-MPC) with a system identification is proposed. The method does not require high-frequency signal injection, but uses the measured values already required for the FCS-MPC. An evaluation of the least squares-based identification on a laboratory test bench showed that the model accuracy and thus the control performance could be improved by an online update of the prediction models.