Model predictive control (MPC) represents an affirmed optimal control strategy, able to handle multivariable systems and their input–output constraints. However, MPC does not provide an integral control action for reference tracking control problems. Several methods have been proposed to overcome this limitation. Standard MPC methods include a disturbance observer to handle unmodeled uncertainties, such as external unknown disturbances and parameter mismatches. Among these formulations, the authors focus on the velocity form MPC, which considers the incremental formulation of the motor state-space model. This formulation gets rid of the bias errors in reference tracking problems. In this article, the MPC paradigm is applied to the current control of synchronous motor drives. The intent is to compare the velocity form and the MPC with disturbance observer. A theoretical analysis of the MPC coupled with disturbance observers and the equivalence between these formulations and the velocity form is presented. Input constraints are included in the MPC optimization process, thus requiring an online quadratic programming solver. Experimental tests consider a 1 kW anisotropic synchronous motor. Numerical aspects regarding the optimization problem are investigated for both methods.

中文翻译：

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同步电机驱动的积分模型预测电流控制


模型预测控制（MPC）代表了一种公认的最优控制策略，能够处理多变量系统及其输入输出约束。然而，MPC 没有为参考跟踪控制问题提供积分控制动作。已经提出了几种方法来克服这个限制。标准 MPC 方法包括一个扰动观测器，用于处理未建模的不确定性，例如外部未知扰动和参数不匹配。在这些公式中，作者重点关注 MPC 的速度形式，它考虑了运动状态空间模型的增量公式。该公式消除了参考跟踪问题中的偏差误差。在本文中，MPC 范式应用于同步电机驱动器的电流控制。目的是将速度形式和 MPC 与扰动观测器进行比较。提出了与扰动观测器耦合的 MPC 的理论分析以及这些公式与速度形式之间的等价性。输入约束包含在 MPC 优化过程中，因此需要在线二次规划求解器。实验测试考虑了 1 kW 各向异性同步电机。这两种方法都研究了有关优化问题的数值方面。