Improving Tracking of Trajectories through Tracking Rate Regulation: Application to UAVs

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 24; p. 9795
• Main Authors: Diaz-Del-Rio, Fernando, Sanchez-Cuevas, Pablo, Iñigo-Blasco, Pablo, Sevillano-Ramos, J L
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13-12-2022; MDPI
• Subjects: Complex systems; Computer Simulation; Control engineering; Controllers; Drone aircraft; error adaptive tracking; Errors; Laws, regulations and rules; Lyapunov stability theory; Methods; mobile robots; Moving targets; path following; Robots; trajectory tracking; UAV; Unmanned Aerial Devices; Unmanned aerial vehicles; Variables; Vehicles; UAV; mobile robots; Lyapunov stability theory; path following; error adaptive tracking; trajectory tracking
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22249795

The tracking problem (that is, how to follow a previously memorized path) is one of the most important problems in mobile robots. Several methods can be formulated depending on the way the robot state is related to the path. "Trajectory tracking" is the most common method, with the controller aiming to move the robot toward a moving target point, like in a real-time servosystem. In the case of complex systems or systems under perturbations or unmodeled effects, such as UAVs (Unmanned Aerial Vehicles), other tracking methods can offer additional benefits. In this paper, methods that consider the dynamics of the path's descriptor parameter (which can be called "error adaptive tracking") are contrasted with trajectory tracking. A formal description of tracking methods is first presented, showing that two types of error adaptive tracking can be used with the same controller in any system. Then, it is shown that the selection of an appropriate tracking rate improves error convergence and robustness for a UAV system, which is illustrated by simulation experiments. It is concluded that error adaptive tracking methods outperform trajectory tracking ones, producing a faster and more robust convergence tracking, while preserving, if required, the same tracking rate when convergence is achieved.