Induction motors are the most widely used motor drives in industry because of its simple construction and other advantages such as reliable operation, low initial cost, easy operation and simple maintenance, high efficiency and having simple control gear for starting and speed control. This usefulness of this motor has resulted into a lot of research including the transient behaviour of the machine. This paper presents a step by step Simulink implementation of an induction machine using dq0 axis transformations of the stator and rotor variables in the arbitrary reference frame. At first the important formulas are stated and then according to these formulas a generalized model of a three phase induction motor is developed and implemented. The model is used to investigate the effects of variations in the machine size and parameter values on the dynamic performance of induction machine. The proposed system has been developed and simulated by using Matlab/Simulink.
Magnetic Levitation System (MLS) is a nonlinear system and it is used extensively in many areas. The system's goal is to use the non-contact principle to magnetize the coil and cause the objects to float to a specific height. The magnetic force and the current flowing through the coil have a nonlinear relationship. In this paper first design of the backstepping controller is done to attain the wanted floating in presence of system related uncertainties and system behavior is witnessed. Then a Sliding Mode Controller (SMC) based on backstepping procedure is formulated. The control gains in this controller are designed in such a way that the characteristics polynomial whose coefficients are control gains is strictly Hurwitz and the closed loop system's asymptotic stability is assured. Simulations are performed and the magnetic ball tracking is observed considering step signal as reference in presence of disturbances. More accuracy is observed in terms of following the reference by the magnetic ball when it is used Sliding Mode Controller based on Backstepping technique instead of backstepping controller by considering pulse type disturbance. By simulation and analyzing results it is confirmed that the proposed control strategy is more effective.
The aim of this study is to design a fuzzy expert system for calculating the health risk level of a patient. The fuzzy logic system is a simple, rule-based system and can be used to monitor biological systems that would be difficult or impossible to model with simple, linear mathematics. The designed system is based on the modified early warning score (MEWS).The system has 5 input field and 1 output field. The input fields are blood pressure, pulse rate, SPO2 ( it is an estimation of the oxygen saturation level in blood. ), temperature, and blood sugar. The output field refers the risk level of the patient. The output ranges from 0 to 14. This system uses Mamdani inference method. A larger value of output refers to greater degree of illness of the patient. This paper describes research results in the development of a fuzzy driven system to determine the risk levels of health for the patients. The implementation and simulation of the system is done using MATLAB fuzzy tool box.
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