Model Predictive Direct Torque Control of Permanent Magnet Synchronous Motors

目前，具有嵌入磁体的永磁同步电机（IPMSM）由于其高功率和转矩密度以及其高效率而主要用于电动和混合动力车辆。为了充分发挥电驱动的性能潜力，适当的驱动控制至关重要。传统的IPMSM驱动控制目前基于PI控制器。相比之下，模型预测控制器（MPC）具有明显更好的性能的潜力：MPC利用待控制系统的过程模型。基于该模型，在每一个采样步骤中确定最优的未来控制变量。然而，这种在线优化对计算硬件提出了很高的要求。由于创新的方法和现在可用的更强大的计算硬件，MPC已经可以用于IPMSM，但这些方法导致其他问题，如高电流和转矩riple.The本项目的目的是开发一个MPC方法，这是专门设计用于汽车传动系统中的IPMSM的控制。因此，要开发的MPC方法基于直接MPC。与经典的MPC相比，在线优化过程中只考虑逆变器的可行电压矢量（根据可能的开关状态）。因此，优化是非常资源有效地执行的，因此实时MPC可以在廉价的计算硬件上实现。由于计算可以很容易地并行化，FPGA或多核架构适合实现。MPC将被实施为直接转矩模型预测控制，由此操作点选择被直接包括在MPC的固有优化算法中。该操作点优化阿利亚有助于最小化损耗。为了分别减少电流和转矩的纹波，重新设计了脉冲模式生成：它可以根据目标函数值进行类似于矢量调制的优化。这导致电流涟漪的减小，尽管控制器周期时间相对较大，使得计算工作量可以由此显著减小。通过使用用于加权目标函数的自优化，在运行时间期间优化每个操作点的转矩动态。

Currently, permanent magnet synchronous motors with embedded magnets (IPMSM) are mainly used for electric and hybrid vehicles due to their high power and torque densities as well as their high efficiency. In order to utilize the full performance potential of an electric drive, an adequate drive control is essential. Conventional drive controls for IPMSM are currently based on PI controllers. In comparison, Model Predictive Controllers (MPC) have the potential of a significantly better performance: A MPC exploits a process model of the system to be controlled. Based on this model optimal future control variables are determined in every sampling step. However, this online optimization sets high demands on the computation hardware. Due to innovative approaches and nowadays available more powerful computing hardware, MPC can already be used for IPMSM, but these approaches lead to other problems like high current and torque ripples.The aim of this project is to develop an MPC approach which is specifically designed for the control of IPMSM in the drivetrain of automobiles. Therefore the MPC approach to be developed is based on Direct MPC. In contrast to classical MPC, only the feasible voltage vectors of the inverter (according to possible switching states) are taken into account during the online optimization. Thus, the optimization is performed very resource-efficient, so real-time MPC can be implemented on inexpensive computation hardware. Since the calculations can be easily parallelized, FPGAs or a multi-core architecture are qualified for the implementation. The MPC will be implemented as a Direct Torque Model Predictive Control whereby the operating point selection is directly included in the inherent optimization algorithm of the MPC. This operating point optimization contributes inter alia to minimize losses. To reduce the ripples of the current and torque, respectively, the pulse pattern generation is redesigned: It can be optimized similarly to vector modulation, depending on the objective function values. This leads to a reduction of the current ripple in spite of a relatively large controller cycle time so that the computing effort can be significantly reduced thereby. By using a self-optimization for weighting the objective function, the torque dynamics for each operating point is optimized during runtime.