Diagnosis of Air-Gap Eccentricity Fault for Inverter Driven Induction Motor Drives in the Transient Condition
Modelling and Simulation Study of a Helicopter with an External Slung Load System
Comparative Study of Single Phase Power Inverters Based on Efficiency and Harmonic Analysis
Trichotomous Exploratory Data Analysis [Tri–EDA]: A Post Hoc Visual Statistical Cumulative Data Analysis Instrument Designed to Present the Outcomes of Trichotomous Investigative Models
LabVIEW Based Design and Analysis of Fuzzy Logic, Sliding Mode and PID Controllers for Level Control in Split Range Plant
Recently, the PID Controllers are widely used in industries for nearly a century due to its features, such as simplicity, flexibility, and efficiency. Recently, the control concepts of non-linear processes in the industries are turned towards the attention of the intelligent controllers, such as Genetic Algorithm tuned PI Controllers, Neural Networks tuned PI Controller, Model Predictive Controller, Nonlinear Adaptive Controller, Fuzzy Logic based Controller, Neuro tuned Predictive Controller, etc. This paper focuses on the Investigation of Genetic Algorithm tuned PI Controller for the Nonlinear Conical tank Level Process. The Conical tank is a highly nonlinear process in which the variation in the area of cross section of the process tank level system with change in shape. In the above work, the Genetic Algorithm tuned PI Controller is especially designed for the control of nonlinear conical tank level process for the exact level maintenance. Also, the designed Genetic Algorithm tuned PI Controller is compared with Conventional PI Controller in Servo operation and Regulatory operation.
Condition monitoring of machines is determination of condition of machine and its change with time. Working condition of machines may be analyzed by measuring physical parameters like: vibration, noise, wear debris, temperature, oil contamination, etc. Wear debris and vibration condition monitoring have great importance in machinery maintenance and fault diagnosis. This paper deals with effective analysis of both combined vibration condition monitoring and wear debris in machinery maintenance and fault diagnosis. Both techniques have their own merits and demerits associated with monitoring and fault diagnosis of machinery. However, it is seen from the past practical experience that using these techniques independently gives a small portion of machine fault diagnosis. By combining both the techniques in machine fault diagnosis it can provide more reliable information. The objective of this paper is to analyse the co-relation between techniques, achieved by experimenting worm gear box at different operating conditions driven by electric motor. Initially, the worm gear box runs under normal conditions. A number of tests has been performed with different contaminant particles added to various lubricants. Wear debris and condition monitoring techniques were studied and results obtained were compared.
Robot manipulators are multi-link nonlinear system with higher uncertainties and disturbances. To obtain desired accuracy and high performance, the controlling of robot should be in real time. Due to higher uncertainties and disturbances like variation in pay load frictions, the performance and accuracy of the system is highly deviated from desired performance and accuracy. This paper presents PD Fuzzy logic controller based scheme to track the desired trajectory of 2-link robot manipulator. In this PD fuzzy logic controller based scheme, the output of the system is fed to Fuzzy logic controller as input. The error and change in error is given as input to Fuzzy logic controller and this control scheme is capable in compensation of disturbances. In MATLAB environment, the simulation is performed and the reference trajectory of robotic system is tracked with minimum error.
From many years in order to maximize the working productivity of the industrial equipment, the control and data acquisition systems have always been available for modernized industrial architecture. However, to increase the reliability of the plant/process, these systems have always tended to be expensive on small and medium scale purposes. Similarly, electric drives find many applications in industries as well as household purposes. Also, for a greater productivity of electric drives, control becomes necessary and will always benefit the end user. This paper presents a microcontrollerbased control and data acquisition system for electric DC drives that is low cost, modular, and can be installed in stages determined by budget constraints or process requirements. The presented system includes a supervisory desktop computer with specifically developed Data Acquisition software. Arduino UNO Microcontroller Development Board is used for drive speed and directional control using Pulse Width Modulation (PWM) technique. Data transfer from desktop to controller and controller to desktop is done using the Serial Port Connectivity (SPC) of the microcontroller. Microsoft Visual Studio is used to develop the necessary GUI Software System. VB.NET is used as the backend programming language for drive control and data acquisition. The data acquired can be stored into Microsoft Excel Workbook for further analysis and the speed v/s voltage characteristics can be saved as an image file.
Nature-inspired optimization algorithms are presented in this paper for the control of brushless direct current (BLDC) motor drive. These algorithms have a number of advantages over classical methods for solving complex optimization problems. The algorithms in this class are based on perceptive behaviour of well ordered members of the population. This type of nature can be found among birds, fishes and insects, such as ants, bees, and the like. These algorithms have mastery over classical methods for solving complex and badly corroborated problems. The key feature of the population algorithms is that the search strategy should be simple and independent. No agent should have dominance over the entire search process. The commutation is implemented electronically in this motor drive using power electronic switches to modify current in the windings based on rotor position. The BLDC motor is just a reversed DC commutator motor, as its conductors remain stationary while magnet rotates facilitating the internal or external position sensors in the motors to sense the actual position of rotor. The rotor position can also be found by the measurement of variations in the Back emf, which is known as sensorless control method. This method reduces the cost as well as the size of the motor as it does not require sensors for determining rotor position. Matlab/simulink is used for modelling drive system. The simulation results with the proposed optimization algorithms are effective in controlling the speed of the drive system.