Analysis of Brushless DC Motor Using Deep Neural Network and BAT Algorithm
Brushless DC (BLDC) motors are specially used in actuation process control, automation industry, spacecraft industry, and military appliances. The model specifications and efficiency parameter depending upon its analysis and design of the BLDC motor are n
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Abstract Brushless DC (BLDC) motors are specially used in actuation process control, automation industry, spacecraft industry, and military appliances. The model specifications and efficiency parameter depending upon its analysis and design of the BLDC motor are necessary to collect consistent data and significant information. The parameter identification is derived by mathematical analysis via optimization techniques using the Deep Neural Network (DNN) and BAT algorithm. The torque, voltage, current, speed, and temperature of Brushless DC motor are analyzed using sensors with PID controller. The analyzed data is to monitor and control through the Internet of things (IOT) using GPRS. Keywords Brushless DC motor · Internet of things (IoT) · BAT algorithm · Deep neural network · PID controller
1 Introduction The monitoring and control of Brushless DC motor drive can be measured in sensorless method, but to scale down the total cost of energizing devices, sensorless methods are used. The merit of sensorless mode of Brushless DC motor is that sensing components and the device can be eliminated, and thus the cost of overall machine drive can be minimized. The demerits of this particular type of sensorless method are most requirements for optimization control algorithms. [1–3]. The Brushless DC motor
K. Balamurugan (B) Department of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore, India e-mail: [email protected] R. Mahalakshmi Department of Electrical and Electronics Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, India
© Springer Nature Singapore Pte Ltd. 2021 P. Suresh et al. (eds.), Advances in Smart System Technologies, Advances in Intelligent Systems and Computing 1163, https://doi.org/10.1007/978-981-15-5029-4_5
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drive has back EMF trapezoidal voltage and it is commonly agitated by 3 sinusoidal current, and consistently mechanized by currents having a square waveform [4–7]. In this paper, speed control and Simulink modeling of Brushless DC motor are presented and the parameter evaluation is done. The primitive strategy of parameter model estimation might be to measure optimal data for a Simulink model which expects reliant variable results or occurrence determined by independent variable parameter inputs. For a certain variable observation, independent variable parameter inputs are classified into input vector and dependent variable parameter outputs are classified into output vector [8]. The optimization techniques like BAT optimization algorithm and Deep Neural Network (DNN) methods are designed for the estimation and control parameters of the Brushless DC motor using Internet of Things. As a new emerging technology said about recent advances in cloud computing, big data analysis, Internet of Things (IoT), modern wireless or sensorless communication has created more attention to a wide number of industrial applications. Internet of Things (IoT) is giving aid to attain the automation process through remote
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