Browsing by Author "Hemakeerthi, D.A.B."
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Item Design & Control of A Dual Wheel Self Balancing Robot(Uva Wellassa University of Sri Lanka, 2020) Liyanage, R.S.; Pushpakumara, P.D.S.; Wijebandara, T.R.; Hemakeerthi, D.A.B.The field of robotics is a playground of creative minds in the modern age. In this research, it designed and controlled a two-wheel self-balancing robot within a low cost and efficient manner. Two-wheel robots can easily be controlled, spin on the spot, or turn around in small places faster than four wheels robots. The main objective of this project is it has a small footprint to navigate nicely through doors and tight spaces and made within low cost. Another specialty of this robot is it acquires it's balanced even within some fluctuations (around 0.35 rad) around its mean position. The angle of the robot relative to the ground will be sensed from the gyroscope. H-bridge motor driver was used to control the motors and two DC gear motors gave force to stable the robot. ATMega microcontroller used to control and connect the modules, sensors. The system is kept balanced in a straight position in the presence of disturbances forces applied by calculating the PID controller. The robot can guide to its destination within an application with a cloud-based platform, through Wi-Fi with the use of Nodemcu. The gyroscope, motors, and control boards were selected by considering both accuracy and cost. The structure of the robot made by low-cost materials. Kalman filter used to eliminate the noise of the gyroscope value. It helps to filter and avoid noises of the robot and get precise angle values to stable the robot smoothly. The fraction of the floor and tires, weight, and height of the robot are the most important factors to calculate the PID values (Kp, Ki, Kd) for the stabilization of the robot. Finally, the self-balancing robot can be made as a very user-friendly, cost-effective, faster, and small size of the product that can be used to carry or send things easily. And also, it can be modified by adding stages with a camera, IR sensor, etc. because wheeled robots can handle higher payload capability and can control the balance by varying the PID values. Keywords: PID controller, Self-balancing, cost effective, Kalman Filter, GyroscopeItem Fuzzy Logic Based Motor Speed Controlling System for Automobile Industry(Uva Wellassa University of Sri Lanka, 2020) Hemakeerthi, D.A.B.; Wijesinghe, W.A.S.Road safety is becoming an important case in all over the world today. The number of deaths is increasing day by day inroads, due to the uncontrollable speed of drivers and accidental sleepiness while driving. The objective of this project is to develop a system to protect both vehicles and passengers on the road. The main aim of this project is to control the speed of a vehicle automatically by controlling the speed of the motor using fuzzy logic. Which reduces the driver’s task for adjusting the gas pedal and checking the speedometer frequently. The research introduced the Fuzzy Logic for speed controlling instead of the commonly used methods such as RF signal controlling method; because Fuzzy logic controllers (FLC) can be used to utilize the human expertise and experience for design controllers. The purpose of using a fuzzy logic-based speed controller is to regulate vehicle speed according to the current speed of the vehicle and the distance between the vehicle and the object in front of the vehicle. The fuzzy controller has developed under three main parts as Fuzzification, Rule base, and Defuzzification. Two inputs were taken to the FLC as ‘Speed error’ and the ‘Distance between vehicle and object Infront’. 49 Fuzzy rules were designed for the fuzzy logic controller in the rule base. The ‘Centroid method’ was used as the defuzzification method. The ‘Mamdani’ system which employs fuzzy sets in the consequent part was used in the Fuzzy logic controller. When the speed and the distance were given, the Fuzzy Inference system gives the best matching fuzzified values as the output value. Then it sends to the motor controller. The results show that the fuzzy logic has minimum transient and steady-state parameters, which show that FLC is more efficient. Finally, the Fuzzy controller produces the responses with little high rise-time, but it offers a high percentage exceed and peak amplitude which can result in poor performance of the system. Keywords: Fuzzy logic, Fuzzy Logic controller (FLC), Rule base, Fuzzification, Defuzzification