Drone reference tracking in a non-inertial frame using sliding mode control based Kalman filter with unknown input

As surprising as it seems, drones and mobile robots in general experience motion sickness when put in a moving environment. This navigation problem has been little if ever explored in the literature. Therefore we propose a formulation of the problem in the simplest possible way as a starting point. The objective of simplifying the problem is to avoid using sophisticated control and measurement devices, such as cameras, and rely instead on control system strategies. In this paper, the moving environment to which is associated a non-inertial frame is considered to have translation motion with respect to the inertial reference frame. The goal is to make the drone track a desired trajectory inside the moving environment based only on the measurements obtained with respect to the non-inertial frame. First, a model representing the dynamics of the drone in the non-inertial frame is developed using the relative motion principles. The new model takes into account the accelerations of the moving environment where they are considered as bounded unknown inputs. Then, a Kalman Filter with Unknown Inputs (KF-UI) is used to estimate simultaneously the states of the drone and the accelerations of the non-inertial frame. Finally, a Sliding Mode controller is implemented. Two numerical simulations were conducted to illustrate the performance of the combined KF-UI and Sliding Mode controller: the first one represents an ideal case where the non-inertial frame's accelerations are constant. The second one illustrates flying a drone in an elevator. The obtained results form an encouraging foundation for follow-on experiments.