Tracking Error Learning Control for Precise Mobile Robot Path Tracking in Outdoor Environment
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10846_2018_916_ReferencePDF.pdf
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1.01 MB
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5486e9d576e49ced334fbc1a128ce000
Author(s)
Kayacan, Erkan
Chowdhary, Girish
Date Issued
August 11, 2018
Publisher
Springer Netherlands
Version
Author's final manuscript
Abstract
Abstract
This paper presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been on-track. In addition to the proof of the stability, it is proven that the cost functions do not have local minima so that the coefficients in TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path tracking performance than the traditional tracking error-based control method. The mobile robot controlled by TELC algorithm can track a target path precisely with less than 10 cm error in off-road terrain.
MIT Department
Senseable City Laboratory
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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DOI of Published Version
https://doi.org/10.1007/s10846-018-0916-3
