文章基本信息

标题：Path Tracking for a Skid-steer Vehicle using Model Predictive Control with On-line Sparse Gaussian Process
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作者：Taewan Kim ; Wonchul Kim ; Seungwon Choi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：5755-5760
DOI：10.1016/j.ifacol.2017.08.1140
语种：English
出版社：Elsevier
摘要：AbstractSkid-steer vehicle can generate a large traction force, which is especially good for navigation on a rough terrain. However, the turning motion is so sensitive to slippage effect that designing a controller is still challenging problem. Also, the motion of the vehicle is affected not only by wheel motion, but also by the road properties and the characteristics of wheel control. With this in mind, we employ a model predictive control (MPC) with an on-line model learning. The velocity model, which represents the relationship between true vehicle velocity and input command, is learned with an on-line sparse Gaussian process (GP). The on-line sparse GP can reduce the computational complexity of GP and also consistently update the model from the driving data. Finally, combining with MPC makes it possible to generate an optimal policy based on the learned model. Experiments are conducted to test the tracking performance of skid-steer robot. The results show the more reliable performance than the method based on a conventional model with parameter adaptation.
关键词：KeywordsPath trackingskid steer vehiclemodel predictive controlsparse gaussian process