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Sparse Identification of Nonlinear Dynamics-based Model Predictive Control for Multirotor Collision Avoidance

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Опубликовано в::arXiv.org (Dec 9, 2024), p. n/a
Главный автор: Jayden Dongwoo Lee
Другие авторы: Kim, Youngjae, Kim, Yoonseong, Bang, Hyochoong
Опубликовано:
Cornell University Library, arXiv.org
Предметы:
Predictive control
Control systems
Collision avoidance
Collision dynamics
Dynamical systems
Nonlinear control
Parameter uncertainty
Constraints
Nonlinear dynamics
Obstacle avoidance
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Краткий обзор:This paper proposes a data-driven model predictive control for multirotor collision avoidance considering uncertainty and an unknown model from a payload. To address this challenge, sparse identification of nonlinear dynamics (SINDy) is used to obtain the governing equation of the multirotor system. The SINDy can discover the equations of target systems with low data, assuming that few functions have the dominant characteristic of the system. Model predictive control (MPC) is utilized to obtain accurate trajectory tracking performance by considering state and control input constraints. To avoid a collision during operation, MPC optimization problem is again formulated using inequality constraints about an obstacle. In simulation, SINDy can discover a governing equation of multirotor system including mass parameter uncertainty and aerodynamic effects. In addition, the simulation results show that the proposed method has the capability to avoid an obstacle and track the desired trajectory accurately.
ISSN:2331-8422
Источник:Engineering Database