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Model predictive control algorithms have recently gained more importance in the field of power electronics and motor drives. One of the important categories of model predictive control methods is improved deadbeat control in which the reverse system model is used to calculate the appropriate inputs for the next iteration of controlling process. In this paper, a new improved deadbeat algorithm is proposed...
This paper deals with fault tolerant model predictive control (MPC) of five-phase brushless direct current (BLDC) motors. The model of machine is used to estimate the stator currents for the next modulation period, and then, the required voltages are calculated to minimize the current errors. Center aligned five-phase space vector modulation (SVM) is used in the inverter section. The proposed controlling...
This paper deals with model predictive control of five phase permanent magnet motors. Using motor model, the stator currents are estimated for the following modulation period, and the required voltages are applied to minimize the current errors. A comparison between the proposed algorithm and conventional cascade PI configuration is conducted.
This study is dealt with fault tolerant control of five phase permanent magnet (PM) machines. The main objectives are to increase the output power while eliminating the generated torque ripples. As a new aspect, the effect of available neutral connection is evaluated on the output power and torque ripples.
This study deals with the fault tolerant vector control strategies of a five-phase permanent-magnet (PM) machine. The analysis is focused on the effect of accessible neutral point under faulty conditions. Open circuit fault of one and two phases are considered, and proper control strategies are proposed to reduce the amplitude of currents in the remaining healthy phases. Simulations under both healthy...
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