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The classic model predictive control presents a variable switching frequency which could produce resonances in the input filter of the matrix converter, affecting the performance of the system. In this paper a model predictive control strategy is proposed with fixed switching frequency operation which is enhanced with an active damping method in order to mitigate resonances of the input filter. The...
This paper proposes a macro-model to simulate of four-switch, three-phase inverter (FSTPI) brushless DC (BLDC) motor drive using switching functions. The proposed model uses inverter switching functions instead of actual circuits, and capable to show the whole steady state and transient performance of the drive. An entire BLDC motor drive including power conversion unit, BLDC motor, current and speed...
Brushless DC (BLDC) motor is attracting much interest due to its good performance for many applications. Moreover, cost reducing of the drive is more attractive for low cost applications. This paper presents the design and implementation of a reduced parts BLDC motor drive. Part reducing is achieved by elimination of three Hall Effect position sensors and reducing the number of power switches to four...
This paper presents the analysis, design, and implementation of a cost-effective sensorless control technique for a low-cost four-switch, three-phase inverter brushless dc motor drive. The proposed sensorless technique is based on the detection of zero crossing points (ZCPs) of three voltage functions that are derived from the filtered terminal voltages nuao and nubo. Six commutation instants are...
Brushless DC (BLDC) motor is attracting much interest due to good performance and ease of control for many applications. Moreover, reducing of the drive components is more attractive for low cost applications. This paper presents the design and implementation of a reduced parts BLDC motor drive using the TMS320LF2407A digital signal processor (DSP) produced by Texas instruments. Part reducing is achieved...
Principle of a new adaptive neuro-fuzzy inference system (ANFIS) with supervisory learning algorithm is introduced and is used to regulate the speed of a four-switch, three-phase inverter (FSTPI) brushless DC (BLDC) drive. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of drive's performance, instead of well-known back propagation learning method, a fuzzy based supervisory...
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