Problem
Research questions and friction points this paper is trying to address.
coaxial drone
underactuation
thrust vectoring
Dynamic Modeling and LQR Control of a Single Coaxial Drone with 2DOF Thrust Vectoring Mechanism
🤖 AI Summary
本文针对共轴旋翼无人机在滚转和俯仰控制中的欠驱动问题，提出了一种带有两自由度推力矢量机构的新设计，并通过LQR控制器进行了动态建模与控制。
📝 Abstract
Coaxial rotor drones have generated considerable interest because of energy efficiency and small size, but they are afflicted with inherent underactuation for roll and pitch control, although systems like swashplates have circumvented this limitation at the cost of greater mechanical complexity. This work presents a novel coaxial drone supplemented by a two-degreesof-freedom pendulum mechanism for active thrust vectoring that offers a less mechanically complicated alternative. We develop a comprehensive Lagrangian dynamic model that does not ignore the inertial contributions of all the components, including body, servo arms, and motor assembly. A Linear Quadratic Regulator(LQR) is designed based on the linearized dynamics around the hover equilibrium. High-fidelity simulations taking actuator dynamics and sensor noise into account validate the proposed architecture. An Extended Kalman Filter (EKF) blends GPS, barometer, and IMU estimates with high accuracy for state estimation. The findings verify the potential and reliability of this approach for power-saving, rapid coaxial UAVs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
2DOF Thrust Vectoring
Lagrangian Dynamic Model
Linear Quadratic Regulator (LQR)
Extended Kalman Filter (EKF)
🔎 Similar Papers
No similar papers found.
