2026-09-20T03:18:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/382832022-01-13T07:54:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
J.J.E. Slotine.
Perk Barıṣ Eren
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
2007-08-03T18:25:27Z
2007-08-03T18:25:27Z
2006
2006
http://hdl.handle.net/1721.1/38283
153291065
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (leaves 77-82).
In this paper, we introduce a framework for learning aggressive maneuvers using dynamic movement primitives (DMP) for helicopters. Our ultimate goal is to combine these DMPs to generate new primitives and demonstrate them on a 3-DOF (3 Degrees of Freedom) helicopter. An observed movement is approximated and regenerated using DMP methods. After learning the movement primitives, the partial contraction theory is used to combine them. We imitate the aggressive maneuvers that are performed by a human and use these primitives to achieve new maneuvers that can fly over an obstacle. Experiments on the Quanser 3-DOF Helicopter demonstrate the effectiveness of our proposed method. In addition, we linearly combine DMPs and propose a new type of DMP. We also analyze Matsuoka's oscillator and Hopf oscillator using contraction theory.
by Barıṣ Eren Perk.
S.M.
82 leaves
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Electrical Engineering and Computer Science.
Control primitives for fast helicopter maneuvers
Thesis
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<Abstract>In this paper, we introduce a framework for learning aggressive maneuvers using dynamic movement primitives (DMP) for helicopters. Our ultimate goal is to combine these DMPs to generate new primitives and demonstrate them on a 3-DOF (3 Degrees of Freedom) helicopter. An observed movement is approximated and regenerated using DMP methods. After learning the movement primitives, the partial contraction theory is used to combine them. We imitate the aggressive maneuvers that are performed by a human and use these primitives to achieve new maneuvers that can fly over an obstacle. Experiments on the Quanser 3-DOF Helicopter demonstrate the effectiveness of our proposed method. In addition, we linearly combine DMPs and propose a new type of DMP. We also analyze Matsuoka's oscillator and Hopf oscillator using contraction theory.</Abstract>
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