Because of the low maintenance and robustness induction motors have many applications in the industries. The speed control of induction motor is more important to achieve maximum torque and efficiency. Various speed control techniques like, Direct Torque Control, Sensorless Vector Control and Field Oriented Control are discussed in this paper. Soft computing technique â€œ Fuzzy logic is applied in this paper for the speed control of induction motor to achieve maximum torque with minimum loss. The fuzzy logic controller is implemented using the Field Oriented Control technique as it provides better control of motor torque with high dynamic performance. The motor model is designed and membership functions are chosen according to the parameters of the motor model. The simulated design is tested using various tool boxes in MATLAB. The result concludes that the
V. Chitra, and R. S. Prabhakar
Induction motors are being applied today to a wider range of applications requiring variable speed. Generally, variable speed drives for Induction Motor (IM) require both wide operating range of speed and fast torque response, regardless of load variations. This leads to more advanced control methods to meet the real demand. The conventional control methods have the following difficulties 1. It depends on the accuracy of the mathematical model of the systems 2. The expected performance is not met due to the load disturbance, motor saturation and thermal variations 3. Classical linear control shows good performance only at one operating speed 4. The coefficients must be chosen properly for acceptable results, whereas choosing the proper coefficient with varying parameters like set point is very difficult To implement conventional control, the model of the controlled system must be known. The usual method of computation of mathematical model of a system is difficult. When there are system parameter variations or environmental disturbance, the behavior of the system is not satisfactory. Usually classical control is used in electrical motor drives. The classical controller designed for high performance increases the complexity of the design and hence the cost. Advanced control based on artificial intelligence technique is called intelligent control. Every system with artificial intelligence is called self-organizing system. On the 80th decade the production of electronic circuits and microprocessors with high computation ability and operating speed has grown very fast. The high power, high speed and low cost modern processors like DSP, FPGA and ASIC ICâ„¢s along with power technique switches like IGBT made the intelligent control to be used widely in electrical drives. Intelligent control, act better than conventional adaptive controls. Artificial intelligent techniques divide two groups: hard computation and soft computation . Expert system belongs to hard computation which has been the first artificial intelligent technique. In recent two decades, soft computation is used widely in electrical drives. They are, 1. Artificial Neural Network (ANN) 2. Fuzzy Logic Set (FLS) 3. Fuzzy-Neural Network (FNN) 4. Genetic Algorithm Based system (GAB) 5. Genetic Algorithm Assisted system (GAA) Neural networks and fuzzy logic technique are quite different, and yet with unique capabilities useful in information processing by specifying mathematical relationships among numerous variables in a complex system, performing mappings with degree of imprecision, control of nonlinear system to a degree not possible with conventional linear systems. Fuzzy logic is a technique to embody human-like thinking into a control system. A fuzzy controller can be designed to emulate human deductive thinking, that is, the process people use to infer conclusions from what they know. Fuzzy control has been primarily applied to the control of processes through fuzzy linguistic descriptions. Fuzzy control system consists of four blocks as shown in Fig. 1. This paper deals about the sandwich of artificial intelligence technique particularly fuzzy logic in the speed control of Induction motor. Various control techniques are discussed in Section II. The Section III describes the block diagram of 3F IM drive along with fuzzy controller. Section IV describes the implementation of maximum torque generation under field oriented control using fuzzy logic controller. Simulation results are given to demonstrate the advantage of proposed scheme is described in Section V. Conclusion and reference studies are mentioned in the last section.
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