Nonlinear Model Predictive Control for Autonomous Quadrotor Trajectory Tracking

It is well known that all the physical devices that surround us, in our daily life are nonlinear systems, the meaning in which the change of the output is not proportional to the change of the input, which results from changes in variables over time, to model such system with the aim to control it, was always an issue for the researchers, where from the literature review, many methods and solutions were created in order to facilitate the task. One of the most efficient approximations was linearizing the system and using linear or nonlinear controllers, so the theoretical part can be more friendly to handle it, the model still is far from reality, therefore it can contain errors and not reliably responds as the real environment. The paper is based on the nonlinear controller system. The controller is embedded with the nonlinear model predictive tool with the aim to track the path executed by the quadrotor. The paper is divided into three sections, the first section highlights a literature review about the efficiency of the linear and nonlinear controllers in the automation and control field, focusing on the nonlinear controllers. In the second section a case study of the nonlinear predictive control application for Tello quadrotor is presented. Finally, the simulation results are discussed, and a comparison is made to understand the potential of using a nonlinear controller instead of a linear one.