Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor

In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation

Traffic Flow Prediction For Intelligent Transportation System Using Machine Learning

This study attempts to develop a model that forecasts precise data on traffic flow. Everything that can impact the flow of traffic on the road is referred to as the traffic environment, including traffic signals, accidents, rallies, and even road repairs that could result in a traffic bottleneck. The driver or passenger can make an informed choice if they have prior knowledge about the vehicle crowd close to the area that will have the greatest impact on traffic. Additionally, it can be utilised in driverless vehicles, which are the automobiles of the future. Today’s traffic is increasing tremendously, and big data transportation concepts are becoming more popular. We are motivated to develop a machine learning model that forecasts traffic flow because the present prediction techniques and models are still insufficient for use in practical applications. The amount of data available to forecast traffic flow is so enormous that it is awkward and laborious. In this work, we intended to evaluate the data for the transportation with significantly less complexity using machine learning and deep learning methods. The user will be informed of the projected information and the constructed machine learning model will predict the traffic flow

Battery Management System with Charge Monitor and Fire Protection for Electrical Drive

The majority of automobile manufacturers currently create electrical vehicles for both two- and four- wheelers. Thus battery becomes essential component and improving methods for calculating a vehicle’s charge capacity. It is required to create and develop an efficient Battery management system so that they should not be over charged or deeply discharged. Electric vehicle an accurate state of charge estimation to reduce the risk of damage, extend their longevity, and safeguard the electronics they power. This project suggests a real-time Battery Monitoring System(BMS) employing the method for State of charge(Soc) and displaying the vital parameters. The suggested BMS is implemented on a hardware platform using the Arduino environment, the proper sensing technology, a central processor, and interface devices

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