A Quadrotor Simulation and Research Platform

The quadrotor is an underactuated, nonlinear system that presents significant challenges in both modeling and control design. This work develops a decoupled control framework based on the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor. A Linear Quadratic Gaussian (LQG) regulator is implemented for control, with two extended Kalman filters employed for state estimation in the respective dynamic subsystems. The full design process, from dynamic modeling to flight simulation presented in detail. Key elements include nonlinear simulation, model linearization, state-space representation, feedforward compensation, Linear Quadratic Regulator (LQR) gain tuning, actuator dynamics, sensor noise, LQG design, and extended Kalman filter. The limitations of applying linear control to a nonlinear system are also presented.

Authors

Publication Details

Journal
Drones and autonomous vehicles/Drones and Autonomous Vehicles
Published
2025-01-01
DOI
https://doi.org/10.70322/dav.2025.10014
Primary Topic
Underwater Vehicles and Communication Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

A Quadrotor Simulation and Research Platform

S. R. Munasinghe
Drones and autonomous vehicles/Drones and Autonomous Vehicles
Underwater Vehicles and Communication Systems
article

A Quadrotor Simulation and Research Platform

S. R. Munasinghe
article en

Abstract

The quadrotor is an underactuated, nonlinear system that presents significant challenges in both modeling and control design. This work develops a decoupled control framework based on the translational (Newtonian) and rotational (Eulerian) dynamics of the quadrotor. A Linear Quadratic Gaussian (LQG) regulator is implemented for control, with two extended Kalman filters employed for state estimation in the respective dynamic subsystems. The full design process, from dynamic modeling to flight simulation presented in detail. Key elements include nonlinear simulation, model linearization, state-space representation, feedforward compensation, Linear Quadratic Regulator (LQR) gain tuning, actuator dynamics, sensor noise, LQG design, and extended Kalman filter. The limitations of applying linear control to a nonlinear system are also presented.

Drones and autonomous vehicles/Drones and Autonomous VehiclesVol. 2(3)
U.S. Department of State, US-UK Fulbright Commission, Bureau of Educational and Cultural Affairs, College of Agriculture and Life Sciences, Cornell University
Openalex Percentile: Top 68%
Underwater Vehicles and Communication Systems
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