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The IUP Journal of Electrical and Electronics Engineering:
Tuning of PI Controllers by GA and PSO Techniques for Sensorless Vector Control
of Induction Motor†
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The paper deals with the design of different control schemes for vector control of induction motor. The conventional control method like Proportional Integral (PI) controller is used to find responses of induction motor like speed and torque and parameters like peak overshoot, peak time and setting time for the above responses. The conventional schemes of motor control are sensitive to parameter changes and hence will not be accurate. The limitations of the above methods are overcome by implementing evolutionary control techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The simulation studies are done in MATLAB/SIMULINK, and the above conventional control method is compared with evolutionary techniques like GA and PSO. The performance characteristics are improved by reducing steady state errors.

 
 

Induction motors play a vital role in the industrial sector, especially in the field of electric drives and control. There are various approaches to creating simulation models of power electronic drives (Sybille and Le-Huy, 2000; and Bedwani and Ismail, 2001). One of the methods is the development of Simulink models in MATLAB/SIMULINK environment. This modeling can be based on a modular approach. The classical or conventional type of control is used in most of the electrical motor drives. It requires a mathematical model to control the system. When there are system parametric variations, the behavior of the system is unsatisfactory and it deviates from the desired performance (Dumitrescu, 1999). Pillay and Levin (1995) developed mathematical models like the dq0 model and the abc models and designed controllers to control the various parameters of the Induction Motor (IM) using the above methods. FOC or vector control (Vas, 1990) of an IM results in decoupled torque and flux dynamics, leading to independent control of the torque and flux as for a separately excited DC motor. FOC has a major disadvantage: they are sensitive to rotor time constant and incorrect flux measurement or estimation at low speeds (Trzynadlowski, 1994). The above-mentioned methods are classical in nature. There are a number of difficulties involved in the design and implementation of conventional controllers for induction machines (Abad et al., 2005). The conventional control uses an accurate mathematical model, which is very difficult to obtain. Of course, it can be obtained using system identification techniques like Proportional Integral (PI) control concepts. In the case of classical control (PI) using linearity concepts, high performance is achieved only for unique operating points. Design of an FLC-based self-tuning PI controller for control of speed in IMs was addressed by Abdessemed (2003). Tuning of PI controllers conventional is by Ziegler-Nichols method. The major drawback of this method is characterized by an overshoot during tracking mode and a poor load disturbance rejection. This is mainly caused by the fact that the complexity of the system does not allow the gains of the PI controller to exceed a certain low value. If the gains of the controller exceed a certain value, the variations in the command torque become too high and will destabilize the system which can be compensated by proper selection of controller variables for tuning (Eberhart and Kennedy, 1995). The conventional schemes of motor control are sensitive to parameter changes and hence will not be accurate. With the advent and rapid growth of digital computers, evolutionary control algorithms using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique are implemented. Eberhart and Kennedy (1995) discussed a new optimizer using particle swarm theory. Del et al. (2008) discussed the basic concepts, variants and applications of PSO. PSO can be used as a power tool in vector control of IM, as suggested by Sajedi et al. (2011). Katiyar (2011) made a comparative study of GA and PSO and revealed that PSO is a better option for optimization problem.

 
 
 

Electrical and Electronics Engineering Journal,Induction Motor (IM), Conventional controllers, Sensorless vector control, Proportional Integral (PI) controller, Genetic Algorithm (GA) controller, Particle Swarm Optimization (PSO) controller.