Coaxial drone uses thrust vectoring for better control and efficiency
Dynamic Modeling and LQR Control of a Single Coaxial Drone with 2DOF Thrust Vectoring Mechanism
Robotics
Summary
Controlling small drones with stacked rotors is tricky because they can’t easily tilt to steer. The authors designed a coaxial drone with a special pendulum mechanism that moves the engine’s thrust direction in two ways, allowing better control without complex parts. They made a detailed math model that includes parts usually ignored, then used a controller to keep the drone stable in the air. Their simulations with real-world factors show this design could save power and respond quickly.
What this means in practice
- •For drone manufacturers: Build lightweight coaxial drones with improved roll and pitch control using a simpler thrust vectoring mechanism to enhance energy efficiency.$Commercial implications: Enables commercial production of power-saving coaxial UAVs with advanced control without complex mechanics.
- •For robotics engineers: Incorporate a 2DOF thrust vectoring mechanism with LQR control for rapid prototyping of agile drones requiring reliable state estimation under sensor noise.
Authors
Ali Jokar, Amin Talaeizadeh, Aria Alasty
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.