Optimal tuning of PID controller for V/f control of linear induction motor using artificial biological intelligence

In order to improve the performance of a Proportional-Integral-Derivative (PID) controller used in the control of Linear Induction Motor (LIM) V/f speed, this research presents a bio-inspired meta-heuristic soft computing approach. A PID controller specifically designed for the LIM system is described in detail, with a focus on how to optimize the controller using an evolutionary strategy that makes use of the Nutcracker Optimizer. Settling time, rise time, maximum overshoot, and ITAE (Integral-Time Absolute Error) are examples of transient response specifications that are achieved in MATLAB/Simulink by using a step input to the LIM and the optimal set of PID parameters chosen from the optimization. In order to determine the most effective technique for obtaining the optimum response in LIM, these outcomes are then contrasted with outcomes from other soft computing techniques.