Robust Nonlinear Model Predictive Control for Quadrotor UAV Trajectory Tracking

DOI

10.2991/978-94-6463-821-9_83How to use a DOI?

Keywords

model predictive control; unmanned aerial vehicle; trajectory tracking

Abstract

This study adapts a nonlinear dynamic predictive model for quadrotor UAV trajectory tracking; which involves translational and rotational motions, the gravitational contribution and gyroscopic impact. In this implementation, Initially construct the mathematical model of UAV dynamics, then derive the analytical Jacobians of the state function serves as a means for improving the real-time optimization outcomes. A closed reference trajectory is given an initial figure-eight pattern and is followed by gentle vertical oscillations to test the controller’s performance. The MPC algorithm is capped with prediction and control horizons to keep the actuator limits in place as well as accelerate maneuverability. Simulation results over a 10‐second period demonstrate effective tracking in the x and z directions. The investigation accentuates the key tasks of weight tuning and constraints formulation that boost airspace quadrotor control’s stability and precision and its potential applications in different environments and the UAV field. Extensive simulations confirm the robustness of the proposed controller effectively.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuwen Zhou
PY  - 2025
DA  - 2025/08/31
TI  - Robust Nonlinear Model Predictive Control for Quadrotor UAV Trajectory Tracking
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 864
EP  - 877
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_83
DO  - 10.2991/978-94-6463-821-9_83
ID  - Zhou2025
ER  -