Tracking Error Learning Control for Precise Mobile Robot Path Tracking in Outdoor Environment
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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.
KeywordsLearning control Mobile robot Path tracking Tracking error
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- 3.Cui, M., Liu, H., Liu, W., Qin, Y.: An adaptive unscented kalman filter-based controller for simultaneous obstacle avoidance and tracking of wheeled mobile robots with unknown slipping parameters. Journal of Intelligent & Robotic Systems. https://doi.org/10.1007/s10846-017-0761-9 (2017)
- 6.Kanjanawanishkul, K., Zell, A.: Path following for an omnidirectional mobile robot based on model predictive control. In: 2009 IEEE International Conference on Robotics and Automation, pp 3341–3346 (2009)Google Scholar
- 7.Kayacan, E., Kayacan, E., Chen, I.M., Ramon, H., Saeys, W.: On the comparison of model-based and model-free controllers in guidance, navigation and control of agricultural vehicles, pp 49–73. Springer International Publishing, Cham (2018)Google Scholar
- 8.Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Modeling and identification of the yaw dynamics of an autonomous tractor. In: 2013 9Th Asian Control Conference (ASCC), pp 1–6 (2013). https://doi.org/10.1109/ASCC.2013.6606388
- 15.Kayacan, E., Zhang, Z., Chowdhary, G.: Embedded high precision control and corn stand counting algorithms for an ultra-compact 3d printed field robot. In: Proceedings of Robotics: Science and Systems. Pittsburgh, Pennsylvania. https://doi.org/10.15607/RSS.2018.XIV.036 (2018)
- 17.Li, M., Imou, K., Wakabayashi, K., Yokoyama, S.: Review of research on agricultural vehiclev autonomous guidance. International Journal of Agricultural & Biological Engineering 2(3), 1–16 (2009)Google Scholar
- 18.Liao, Y., Ou, Y., Meng, S.: Wheeled mobile robot based on adaptive linear quadratic gaussian control. In: 2017 29Th Chinese Control and Decision Conference (CCDC), pp 5768–5775 (2017). https://doi.org/10.1109/CCDC.2017.7978197
- 22.Pan, Y., Cheng, C.A., Saigol, K., Lee, K., Yan, X., Theodorou, E., Boots, B.: Agile autonomous driving using end-to-end deep imitation learning. In: Proceedings of Robotics: Science and Systems. Pittsburgh, Pennsylvania. https://doi.org/10.15607/RSS.2018.XIV.056 (2018)