Modelling and Control of Electric Motor Drives Using New Chaotic Gorilla Troops Optimisation

Abstract

In this dissertation, four new sets of chaotic artificial gorilla troop optimizer are created. The position update rule is chaotically varied to yield better performance of the parent technique. There are two controlling parameters in the algorithm which are modified using chaos maps. Both of them are varied together too. Moreover, the position update equation as well as the controlling parameters is altered with the help of chaos maps to get improved performance. A total of ten well known and widely referenced one-dimensional chaos maps are utilized to develop new chaotic methods. Unimodal and multimodal benchmark functions are used to evaluate the efficacy of the proposed methods. By utilizing the delta operator, it is possible to reduce two induction motor models using this algorithm. Not only the statistical measures of the optimized values are considered for the study but also two popular non-parametric tests have also being carried out to verify the significance of the outcomes thus obtained. The results of the 50 hp and 500 hp induction motor have been presented. A framework for approximating model matching is used in both cases to implement the PID controller. Further an induction motor drive is also modeled and its PI controller is proposed using the unified delta operator. The proposed methods outperform current standard and cutting-edge methods as being evident from their convergence plots as well.