Sudeshna Ghosh, Harsh Goud, Pankaj Swarnkar, and Dinesh M. Deshpande
Induction motor (IM), PID controller, genetic algorithm (GA), artificial bee colony (ABC) optimization
Induction motor (IM) control has been posing many challenges to the researchers over the last many decades. Design and implementation of suitable control algorithm is difficult in variable frequency drives employing IMs due to their highly nonlinear model. This article presents the design and implementation of adaptive proportional– integral–derivative (PID) controller tuned with genetic algorithm (GA) and artificial bee colony (ABC) optimization techniques for controlling IM speed and torque. The impact of parameter variation can be effectively reduced by accurately tuning the adaptive PID controller, thus ensuring finest speed response of IM drive. The most optimized controller parameters Kp, Ki and Kd are obtained by minimizing the cost function which is the integral time absolute error criterion, thus guiding GA and ABC algorithms to get the optimized controller parameters. Detailed comparison is presented based on simulation studies for improving transient and steady-state response with GA, ABC algorithms with online tuning and Chien, Hrones and Reswick (CHR) methods. Out of all the four techniques, ABC demonstrated superior performance compared to online tuning, CHR tuning and GA-based tuning.
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