Abhishek Singh - Academia.edu (original) (raw)
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Papers by Abhishek Singh
Soft Computing Research Society eBooks, 2023
This paper presents a comparative study on speed control of brushless DC motor, which has wide ap... more This paper presents a comparative study on speed control of brushless DC motor, which has wide applications in electrical vehicles, manufacturing plants, aerospace, etc. Initially, the proportional controller is implemented using the developed mathematical model of BLDC motor. Then, the PID and PII controllers are implemented with speed as its returning path to increase the performance of speed control. The optimum values of PID and PII parameters are evaluated using performance index based constrained optimization. The integral square error is used as a performance index to form the objective function. The objective function is evaluated for different values of parameters using non-linear constrained optimization. The performance is further increased by introducing variable parameters using neural network-based gain scheduling control. The neural network-based control offers better properties such as low overshoot and provides lower susceptibility to parameter variations. To show the effectiveness of the presented approach, extensive simulations are carried out in MATLAB environment.
Soft Computing Research Society eBooks, 2023
This paper presents a comparative study on speed control of brushless DC motor, which has wide ap... more This paper presents a comparative study on speed control of brushless DC motor, which has wide applications in electrical vehicles, manufacturing plants, aerospace, etc. Initially, the proportional controller is implemented using the developed mathematical model of BLDC motor. Then, the PID and PII controllers are implemented with speed as its returning path to increase the performance of speed control. The optimum values of PID and PII parameters are evaluated using performance index based constrained optimization. The integral square error is used as a performance index to form the objective function. The objective function is evaluated for different values of parameters using non-linear constrained optimization. The performance is further increased by introducing variable parameters using neural network-based gain scheduling control. The neural network-based control offers better properties such as low overshoot and provides lower susceptibility to parameter variations. To show the effectiveness of the presented approach, extensive simulations are carried out in MATLAB environment.