Time and output warping of control systems: Comparing and imitating motions

Kingston, Peter; Egerstedt, Magnus

doi:10.1016/j.automatica.2011.03.004

Cited by 6 publications

(1 citation statement)

References 16 publications

(12 reference statements)

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“…For instance, a tracking rate that adapts to system errors has been used in [3] to improve the TT guidance results for underactuated vehicles in the presence of parametric modeling uncertainties, although these authors use the term 'path following' to refer to their implementation. There are other approaches, such as the one inspired by the Dynamic Time Warping (DTW) algorithm (studied extensively in the automatic speech recognition literature) in [4], where a strictly increasing rate of progression ( . r > 0) is selected by minimizing a cost function for finite-duration movements.…”

Section: Introductionmentioning

confidence: 99%

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

Díaz-del-Río

Sanchez-Cuevas

Iñigo-Blasco

et al. 2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%