Predictive Analysis using deep learning techniques are emerging in several engineering domains. Artificial Neural Networks consists of network of layers to learn, process and predict from the data. Their implementation in electrical and electronics engineering has consistently helped in producing intelligent industries with effective results. The brushless direct current (BLDC)motor generates magnetic fields by switching DC current to the motor windings using electronic closed-loop controllers. BLDC motors require low maintenance, and exhibit high speed and sufficient torque capacity and hence are used in various applications. This motor has an edge over other motors because of its superior performance and ease with which the power converters can regulate its speed. This article describes a technique for varying the speed of a BLDC motor that involves altering the voltage of bridge converter, which feeds the motor winding. The speed control is carried out with a speed controller (PI-based). The motor is modelled in MATLAB/Simulink, and a PI controller is employed to provide the speed control. Simulated waveforms of EMF signals are achieved along with rotor speed, Hall Effect signals, electromagnetic torque, and PWMsignals. Artificial neural networks (ANN) are used to forecast the corresponding parameters, and they are fed with the gathered data to produce results that are reasonably close to the results from the simulations. Hence, both the simulation-based approach as well as the predictions from the data provided, yield satisfactory outcomes.

Deep Learning Based Predictive Analysis of BLDC Motor Control

Predictive Analysis using deep learning techniques are emerging in several engineering domains. Artificial Neural Networks consists of network of layers to learn, process and predict from the data. Their implementation in electrical and electronics engineering has consistently helped in producing intelligent industries with effective results. The brushless direct current (BLDC)motor generates magnetic fields by switching DC current to the motor windings using electronic closed-loop controllers. BLDC motors require low maintenance, and exhibit high speed and sufficient torque capacity and hence are used in various applications. This motor has an edge over other motors because of its superior performance and ease with which the power converters can regulate its speed. This article describes a technique for varying the speed of a BLDC motor that involves altering the voltage of bridge converter, which feeds the motor winding. The speed control is carried out with a speed controller (PI-based). The motor is modelled in MATLAB/Simulink, and a PI controller is employed to provide the speed control. Simulated waveforms of EMF signals are achieved along with rotor speed, Hall Effect signals, electromagnetic torque, and PWMsignals. Artificial neural networks (ANN) are used to forecast the corresponding parameters, and they are fed with the gathered data to produce results that are reasonably close to the results from the simulations. Hence, both the simulation-based approach as well as the predictions from the data provided, yield satisfactory outcomes. 

The BLDC motors are extensively used in aerospace, electric vehicles, medical equipment, etc., owing to their outstanding speed–torque characteristics. However, BLDC motors require controllers to control the speed, torque and output power based on the application. The PID, fuzzy and ANN-based controllers that are used for the control of BLDC motor have limitations due to their design complexity and implementation. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been developed to control the speed of BLDC motor drive and the simulation results are investigated and compared with existing control techniques under the specified operating conditions.

High-Performance ANFIS-Based Controller for BLDC Motor Drive

Third and higher order systems are used in many control applications. Real-time data of the uncontrolled system is required for the design of suitable controller and achieve the desired response from such systems. Implementing a sophisticated process involves lot of components and subsystems. This will involve lot of energy consumption and cost expenditure in implementation. In this article, Pspice is used as an effective tool for creating an equivalent process using physically realizable electronic components, and the dynamic behaviour of higher order systems for parameter variation is predicted. This approach will be useful to obtain the desired time response of such systems and will facilitate in implementation of the actual system based on the inference from the Pspice simulation.

Design and Analysis of a Higher Order Process by Employing Pspice Modelling

The data to train autonomous cars was not so abundant before a few years. After Waymo released their driving data, it is widely used in academic research. It consists of a huge amount of great quality images which is collected from various driving scenarios. Reliable and authenticated driving policies have crucial roles to develop efficient automated driving systems. This becomes one of the fundamental challenges for researchers to find the precise solution for the same. Academic researchers need to make several assumptions for the implementation of automated vehicle parts in their simulations or models. These assumptions may not be relevant to the real-time interactions as per the simulation-based research. Since the internal driving policy is under proprietary protection, researchers need to design robust and reliable policies to implement automated driving parts using deep learning models. This paper analyzes several deep learning systems to learn autonomous driving behavior using Waymo's dataset. In addition, this article provides an extensive overview of different aspects of designing automated driving simulators.

An Overview of Deep Learning Techniques for Autonomous Driving Vehicles

This paper discusses the development and performance analysis of ANN-based reference model controller and ANN-based self-tuned PID controller for BLDC motor drives As the BLDC motor drives are nonlinear due to its parameter and load variations, there is a need to develop ANN-based controllers to overcome the problems arising due to nonlinearity in BLDC motor drives In this paper, ANN-based self-tuned PID controller is developed for speed control of BLDC motor drives and its performance is compared with the standard ANN-based reference model-controlled BLDC motor drives The unique feature of ANN-based self-tuned PID controller is that it can dynamically change the PID controller gains to provide optimum performance under changing dynamics of BLDC motor drive Experimental results show that ANN-based self-tuned PID-controlled BLDC motor drives can effectively deal with speed tracking, load variations, and parameter variations

ANN-Based Controllers for Improved Performance of BLDC Motor Drives

Most of the real-time systems used in control applications are of higher order. Implementation of the controller requires dynamic and intelligent tuning to achieve the desired time response in control applications. An attempt is made in this article to extend the use of a simple first-order non-iterative compensator used in a Brushless Direct Current (BLDC) drive-based position control system to type 1 higher order systems. The compensator is found to yield the desired frequency response specifications with lesser overshoot and settling time than those of the conventional lag and lead compensators for various type 1 higher order systems.

A. Singaravelan

Papers

ANN-Based Controllers for Improved Performance of BLDC Motor Drives

Design of a Non-iterative First-Order Compensator for Type 1 Higher Order Systems