Reduced Order Modelling and Control of Induction Motor with Chaos-Based Gorilla Troop Optimizers (CBGTO)

Abstract

This paper proposes 50 new chaos-based gorilla troop optimizers (CBGTO). It is possible to change two regulating parameters in the parent algorithm by using various one-dimensional chaotic maps. Along with these two controlling variables, one of the random variables is also changed in order. Ten well-known and widely used chaotic maps are used to create chaotic algorithms. An induction motor model of order five is used to evaluate the performance of the suggested method. In addition, the same approach is used to build an intelligent PID controller. By using the delta operator, the induction motor model is made simpler. The applicability of the findings is confirmed by statistical analyses of the optimised values, in this example, the integral of time-weighted absolute error (ITAE). Results for the test system under consideration, a 50 hp induction motor, are remarkably positive. Additional parameter changes could be done in the future to boost the functionality of the gorilla troop optimizer (GTO). The PID controller is put into action using a framework for approximate model matching. The novel methods outperform the existing standard and state-of-the-art methods in terms of convergence speed and accuracy.