The PID control method is used extensively for controlling UAVS and other systems. Usually, the controller design and tuning process assumes a linear system with known dynamics, making it vulnerable to high non-linear changes, such as variations in load and environment uncertainties.
In this project, we will explore a controller implementation using neural networks, subject to small non-linearities.
We will first build a simulator for a non-linear system of a single AC motor with a load. Next, we will design a framework for learning the controller and make experiments using a PID benchmark in several situations, such as varying load and friction.