Due to its fault tolerance, a multiphase brushless direct current (BLDC) motor can meet high reliability demand for application in electric vehicles. The voltage-source inverter (VSI) supplying the motor is subjected to open circuit faults. Therefore, it is necessary to design a fault-tolerant (FT) control algorithm with an embedded fault diagnosis (FD) block. In this paper, finite control set-model predictive control (FCS-MPC) is developed to implement the fault-tolerant control algorithm of a five-phase BLDC motor. The developed control method is fast, simple, and flexible. A FD method based on available information from the control block is proposed; this method is simple, robust to common transients in motor and able to localize multiple open circuit faults. The proposed FD and FT control algorithm are embedded in a five-phase BLDC motor drive. In order to validate the theory presented, simulation and experimental results are conducted on a five-phase two-level VSI supplying a five-phase BLDC motor.

This paper presents a new control strategy based on finite-control-set model-predictive control (FCS-MPC) for Neutral-point-clamped (NPC) three-level converters. Containing some advantages like fast dynamic response, easy inclusion of constraints and simple control loop, makes the FCS-MPC method attractive to use as a switching strategy for converters. However, the large amount of required calculations is a problem in the widespread of this method. In this way, to resolve this problem this paper presents a modified method that effectively reduces the computation load compare with conventional FCS-MPC method and at the same time does not affect on control performance. The proposed method can be used for exchanging power between electrical grid and DC resources by providing active and reactive power compensations. Experiments on three-level converter for three Power Factor Correction (PFC), inductive and capacitive compensation modes verify the good and comparable performance. The results have been simulated using MATLAB/SIMULINK software.

In many signal processing applications, one wishes to acquire images that are sparse in transform domains such as spatial finite differences or wavelets using frequency domain samples. For such applications, overwhelming empirical evidence suggests that superior image reconstruction can be obtained through variable density sampling strategies that concentrate on lower frequencies. The wavelet and Fourier transform domains are not incoherent because low-order wavelets and low-order frequencies are correlated, so compressive sensing theory does not immediately imply sampling strategies and reconstruction guarantees. In this paper, we turn to a more refined notion of coherence-the so-called local coherence-measuring for each sensing vector separately how correlated it is to the sparsity basis. For Fourier measurements and Haar wavelet sparsity, the local coherence can be controlled and bounded explicitly, so for matrices comprised of frequencies sampled from a suitable inverse square power-law density, we can prove the restricted isometry property with near-optimal embedding dimensions. Consequently, the variable-density sampling strategy we provide allows for image reconstructions that are stable to sparsity defects and robust to measurement noise. Our results cover both reconstruction by ℓ1-minimization and total variation minimization. The local coherence framework developed in this paper should be of independent interest, as it implies that for optimal sparse recovery results, it suffices to have bounded average coherence from sensing basis to sparsity basis-as opposed to bounded maximal coherence-as long as the sampling strategy is adapted accordingly.

有限状态预测控制能够实现静止坐标系下电压型PWM整流器的电流控制, 但开关频率及稳态控制精度方面的缺陷限制了其进一步发展。针对这一问题, 提出了基于最优时间序列的电压型PWM整流器电流控制策略。根据电网电压相位信息筛选非零矢量, 基于电流变化特性预测模型得出不同矢量作用下的电流变化特性, 结合电流给定值构建基于α、β 轴电流误差的价值函数, 以单个采样周期内电流误差最小为约束条件确定非零矢量作用时间。实验结果表明, 本文提出的基于最优时间序列的预测控制策略能够有效弥补有限状态预测控制在开关频率及稳态控制精度方面的缺陷, 在保证稳态控制精度的同时, 实现静止坐标系下电流的准确、快速调节。

有限状态预测控制能够实现静止坐标系下电压型PWM整流器的电流控制, 但开关频率及稳态控制精度方面的缺陷限制了其进一步发展。针对这一问题, 提出了基于最优时间序列的电压型PWM整流器电流控制策略。根据电网电压相位信息筛选非零矢量, 基于电流变化特性预测模型得出不同矢量作用下的电流变化特性, 结合电流给定值构建基于α、β 轴电流误差的价值函数, 以单个采样周期内电流误差最小为约束条件确定非零矢量作用时间。实验结果表明, 本文提出的基于最优时间序列的预测控制策略能够有效弥补有限状态预测控制在开关频率及稳态控制精度方面的缺陷, 在保证稳态控制精度的同时, 实现静止坐标系下电流的准确、快速调节。

传统的模型预测控制(model predictive control,MPC)在一个控制周期内仅可输出单一的离散电压矢量,考虑到变流器拓扑可提离散电压矢量的局限性,三相脉宽调制(pulse width modulation,PWM)整流器存在较大的稳态功率脉动。在分析三相PWM整流器离散预测模型的基础上,提出一种模型预测虚拟电压矢量(model predictive virtual voltage vectors,MPV³)控制方法,通过构建6个虚拟的电压矢量代替传统MPC中的8个离散电压矢量,旨在拓展系统的控制集覆盖范围。首先,根据最近三矢量原则确定构建虚拟电压矢量所需的离散电压矢量,接着以功率无差拍跟踪控制为指标在线求取各离散电压矢量作用时间。为了保证虚拟电压矢量的可实现性,构建相应的目标函数进行矢量作用时间的可行性判断和动态修正。最后,为验证所提MPV³方法的可行性和有效性,分别与传统MPC和占空比优化进行仿真和实验的对比分析,并进一步归类总结出各方法的优缺点及适用范围。

传统的模型预测控制(model predictive control,MPC)在一个控制周期内仅可输出单一的离散电压矢量,考虑到变流器拓扑可提离散电压矢量的局限性,三相脉宽调制(pulse width modulation,PWM)整流器存在较大的稳态功率脉动。在分析三相PWM整流器离散预测模型的基础上,提出一种模型预测虚拟电压矢量(model predictive virtual voltage vectors,MPV³)控制方法,通过构建6个虚拟的电压矢量代替传统MPC中的8个离散电压矢量,旨在拓展系统的控制集覆盖范围。首先,根据最近三矢量原则确定构建虚拟电压矢量所需的离散电压矢量,接着以功率无差拍跟踪控制为指标在线求取各离散电压矢量作用时间。为了保证虚拟电压矢量的可实现性,构建相应的目标函数进行矢量作用时间的可行性判断和动态修正。最后,为验证所提MPV³方法的可行性和有效性,分别与传统MPC和占空比优化进行仿真和实验的对比分析,并进一步归类总结出各方法的优缺点及适用范围。