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Path Tracking for a Skid-steer Vehicle using Learning-based Model Predictive Control

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Authors
김태완
Advisor
김현진
Major
공과대학 기계항공공학부
Issue Date
2017-02
Publisher
서울대학교 대학원
Keywords
Path trackingskid-steer vehiclemodel predictive controlsparse Gaussian process.
Description
학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2017. 2. 김현진.
Abstract
Skid-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 a 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 a skid-steer robot at the indoor and the outdoor environment. The results show the more reliable performance than the method based on a conventional model with parameter adaptation.
Language
English
URI
https://hdl.handle.net/10371/123947
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Theses (Master's Degree_기계항공공학부)
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