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Inverse Reinforcement Learning Control for Trajectory Tracking for a Quadrotor UAV

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Authors
최승원
Advisor
김현진
Major
공과대학 기계항공공학부
Issue Date
2014-02
Publisher
서울대학교 대학원
Keywords
Apprenticeship learningInverse reinforcement learningQuadrotor trajectory tracking
Description
학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2014. 2. 김현진.
Abstract
The main purpose of this thesis is to imitate the demonstrations of a quadrotor UAV flown by an expert pilot. First, we collect a data set of several demonstrations by an expert for a certain task which we want to learn. We extract a representative trajectory from the dataset. Hidden Markov model (HMM) and dynamic time warping (DTW) are used for obtaining the trajectory. We extract the sequence of state and input data. But a direct use of the input data can cause the danger in stability. For that reason, a controller is required. We design a reinforcement learning controller with reward function of linear quadratic form. To track the extracted trajectory well, an inverse reinforcement learning algorithm is suggested. Using particle swarm optimization (PSO), the reward function that minimizes the trajectory tracking error is learned. With the simulation and experiment applied to a quadrotor UAV, the successful imitation result is presented.
Language
English
URI
http://hdl.handle.net/10371/123761
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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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