Indirect field-oriented controlled of induction motor using fuzzy logic technique - Wan Abd Malik, Wan Mohamad Khairudin (2012)
In modern application of electric motors, wide ranges of speed and torque are required to drive the industrial processes, electric vehicles, water pumps, home appliances and others. While some of the processes require various speeds with constant torque, other processes require various torqueses with constant speed or both. These processes are using continuous, non-continuous, linear and non-linear loads. Many control methods have been designed and implemented to increase quality, process efficiencies and reduction in energy consumption. The Indirect Field Oriented Control (IFOC) is a method of controlling three-phase induction motor which converts the three-phase induction motor into a linear device where the torque and the flux in the motor can be controlled independently. The aim of this work is to simulate IFOC in MATLAB/Simulink and further to implement the system using MATLAB/Simulink and Code Composer Studio (CCS) software with DSPF2812 controller board. The simulations of IFOC system include Proportional / Integral (PI) and Fuzzy Logic controller. The simulation will determine response and stability of the system. The system implementation is divided into three steps. The first step is to design the system using Matlab/Simulink software. This includes the IFOC system, parameter setting and configuration of the controller board input and output interfaces. Second part is to develop three-phase inverter, driver circuit and signal conditioning circuit. The third part is to test the IFOC system and to record the response of the system in both simulation and implementation works. The results show the speed response with different PI gain setting and Fuzzy Logic controller. The configuration of the controller board is done through MATLAB DSP toolbox. New Fuzzy controller is designed using MATLAB/Simulink block set and direct implementation through the software is introduced. The results showed that Fuzzy Logic controller produced better response as compared to PI.