Abstract

For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. This paper proposed the implementation of adaptive neuro fuzzy inference system i.e. ANFIS for controlling the various parameter of three phase induction motor. The various parameter of induction motor is flux, load, torque, terminal voltage, speed, rotor angle, stator currents, slip, id, iq, rotor currents v/s time this shows their performance characteristic on respective scope. The proposed neuro-fuzzy controller incorporates fuzzy logic algorithm with a five-layer artificial neural network (ANN) structure. The performance of the proposed neuro-fuzzy based vector-controlled induction motor drive is investigated at different operating conditions. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. This integrated approach improves the system performance, cost-effectiveness, efficiency, dynamism, reliability of the designed controller. Fuzzy based controller develops a control signal which yields on the firing of the rule base, which is written on the previous experiences & these rules are fired which is random in nature. This result shows, the outcome of the controller is also random & optimal results may not be obtained. Selection of the proper rule base depending upon the situation can be achieved by the use of an ANFIS controller, which becomes an integrated method of approach for the control purposes & yields excellent results, which is the highlight of this paper

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