In motor drive systems based on model predictive control, a mathematic model of the motor is used to predict the future behavior of the system. However, the parameters in the motor model may not match their real values since these parameters may vary under different operation conditions. All parameter variations result in inaccurate predictions, and influence the steady-state control performance of the whole control system. In this paper, an improved model predictive control method is presented. Firstly, when parameter mismatches exist, the sources of the current prediction error are analyzed. It is revealed that current prediction error is directly affected by a prediction model with parameter mismatches and inaccurate one-step delay compensation. In particular, the influence form one-step delay compensation is discussed in this paper. Then a reaching-law-based sliding mode discrete observer is introduced to implement accurate one-step delay compensation and to observe all parameter variations. Finally, a predictive control method combined with sliding-mode discrete observation is presented to reduce parameter sensitivity. Simulation and experimental results show that the proposed method can increase the robustness of model predictive control systems.

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基于改进预测模型的永磁同步电机驱动模型预测电流控制方法

在基于模型预测控制的电机驱动系统中，电机的数学模型用于预测系统的未来行为。但是，电机模型中的参数可能与其实际值不匹配，因为这些参数在不同的操作条件下可能会有所不同。所有参数变化都会导致预测不准确，影响整个控制系统的稳态控制性能。本文提出了一种改进的模型预测控制方法。首先，当存在参数不匹配时，分析当前预测误差的来源。结果表明，当前预测误差直接受到具有参数不匹配和不准确一步延迟补偿的预测模型的影响。特别地，本文讨论了一步延迟补偿的影响。然后引入基于到达律的滑模离散观测器来实现精确的一步延迟补偿并观察所有参数变化。最后，提出了一种结合滑模离散观测的预测控制方法来降低参数敏感性。仿真和实验结果表明，该方法可以提高模型预测控制系统的鲁棒性。