Genetic algorithms(GA) is optimization technique used in the equivalent load test of the induction motors to select the values of the factors(modulation indicators) that effect on the performance properties in terms of the values of the currents and the total loss within the machine. One way to choose these parameters is by trial and error while this paper based on GA method to improve the parameters selection. A model is designed to simulate the loading of the induction motor and obtain its own results by the MATLAB program 2017a. There are different methods used to achieve this task such as PWM inverter with different modulation techniques, Constant Voltage Variable Frequency (CVVF) method, Variable Voltage Constant Frequency (VVCF) method and Variable Voltage Variable Frequency (VVVF) method.

The main focuses are to design controlling systems of good disturbance, stability rejection, and small error-tracking. Trajectory tracking of robot manipulators are controlled by several methodologies, but when robot manipulator works with uncertain dynamic models, some limitations of this technique appear. Concerning the control perspective, such uncertainty can be divided into two groups: the unstructured inputs (e.g. disturbance effect) and the structure dynamics (e.g. the changes of parameter). Within a small number of applications, some environments, could be unknown or unstructured, make use of robot manipulators, along with some tools of strong mechanics also can make use of new methods of control to design a controller of nonlinear robust with a reasonable performance. So in this paper we test the effect of disturbance in control the first DOF of PUMA 560 using non model based FO-Fuzzy-PID controller and compared its results with two model based controllers (CTC, ANN). Also we study the effect of change of inertias parameters in the 2 cases Model based control and non- Model based control and then discus which controller give the best results. The main objective of this paper is that the non model based FO-Fuzzy-PID is able to emulate the manipulator dynamic behaviour without the need to have a complex nonlinear mathematical model for the robot.