Grey Wolf Optimizer (GWO) for Field Oriented Control Traction Motor of Light Rail Transit Jabodebek Train

The density of population in the city of Jakarta has led to an increase in traffic. This of course can damage air quality due to carbon gas emissions released from motorized vehicles. As a result of the decline in air quality, it can cause problems with the health of people living around the city of Jakarta. To overcome these problems, the local government is trying to provide mass transportation that can reduce traffic density, one of which is the Jabodebek LRT Jabodebek. LRT runs using a control system on the taxi motor. Traction motor control problems are a major concern so that the train can run properly. In this study, a simulation of the traction motor movement of LRT Jabodebek is carried out when passing through a path with an increase of $\mathbf{2. 9 \%}$ using the Field-Oriented Control (FOC) technique. The FOC technique is used to control the speed of the traction motor. The load on the train is made fixed with a gradual weight from the low point to the highest point. The tuning technique in this study was carried out using hand-tuning and artificial intelligence techniques, namely the gray wolf optimizer. ITAE (Integral of Time multiplied by Absolute Error) is used as an objective function to calculate the difference between actual speed and reference speed. The results of this study show that the grey wolf optimizer has excellent results with Kp and Ki values are 16.233861 and 0.526774 capable of restabilizing the speed due to load spikes caused by the train passing through the rising track.