Dynamics modeling and control of mechanical systems using machine learning approaches and their applications to a quadrotor UAV기계학습 기법을 활용한 기계적 시스템의 동역학 모델링과 제어 및 쿼드로터 무인기로의 활용

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dc.contributor.advisorBang, Hyochoong-
dc.contributor.authorLee, Seongheon-
dc.description학위논문(박사) - 한국과학기술원 : 항공우주공학과, 2021.2,[iv, 114 p. :]-
dc.description.abstractIn the majority of engineering problems, modeling a dynamic system usually concerned with estimating the motion of a rigid body on a Euclidean space by means of Newtonian mechanics. In Newtonian mechanics, as far as we can keep track of all the forces acting on entire bodies, we can estimate the motion of the bodies, which means the dynamic characteristics of the system can be explicitly understandable. When the system configuration is just a bit more complicated, however, it is difficult to analyze the relationship between the forces acting between the objects, and the Newtonian approach cannot easily applicable. In the field of aerospace engineering, for example, a single aircraft can be easily modeled as a rigid body. Yet, simple applications such as controlling an object suspended from an aircraft, designing a slung load transportation system with multiple UAVs, and stabilizing an inverted pendulum on top of a multi-rotor aircraft are considered to be difficult problems from the Newtonian perspective. Therefore, in this dissertation, we propose machine learning approaches in modeling and control of a dynamic system, to which the analytic solution cannot be easily achievable. To this end, we exploit differential geometry and Lagrangian mechanics to fit the problem into a differentiable program, which is far from the previous approaches that train a neural network with supervised learning by simulating given dynamics. Simply speaking, we get the next state from an automatic differentiation of a Lagrangian, which is a scalar Energy-related function. We also exploit the reinforcement learning approach to control the auto-generated system in a single machine learning pipeline. As an example, to verify the feasibility of the proposed method, the attitude dynamics of the multi-rotor UAV, which evolves on the special orthogonal group SO(3) is simulated. In addition, by designing an attitude controller with a geometric control and reinforcement learning approaches, we describe a series of processes required in dynamic modeling and control of a mechanical system.-
dc.subjectmultirotor UAV▼amachine learning▼aartificial intelligence▼adifferentiable programming▼aautomatic differentiation▼areinforcement learning▼aLagrangian mechanics▼adifferential geometry-
dc.subject멀티콥터 무인기▼a기계학습▼a인공지능▼a미분가능 프로그래밍▼a자동 미분▼a강화학습▼a라그랑주 역학▼a미분 기하학-
dc.titleDynamics modeling and control of mechanical systems using machine learning approaches and their applications to a quadrotor UAV-
dc.title.alternative기계학습 기법을 활용한 기계적 시스템의 동역학 모델링과 제어 및 쿼드로터 무인기로의 활용-
dc.description.department한국과학기술원 :항공우주공학과,-
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