Robust H-Infinity Dual Cascade MPC-Based Attitude Control Study of a Quadcopter UAV

Aimed at the stability problem of quadrotor Unmanned Aerial Vehicle (UAV) flight attitudes under random airflow disturbance conditions, a robust H-infinity-based dual cascade Model Predictive Control (MPC) attitude control method is proposed. Model Predictive Control itself has the capability to min...

Full description

Saved in:
Bibliographic Details
Published in:Actuators 2024-10, Vol.13 (10), p.392
Main Authors: Hui, Nanmu, Guo, Yunqian, Han, Xiaowei, Wu, Baoju
Format: Article
Language:English
Subjects:
Air flow
Algorithms
Analysis
Angular velocity
Attitude control
Control algorithms
Control methods
Control stability
Control theory
Controllers
Deviation
Drone aircraft
dual cascade MPC control
H-infinity control
Methods
model predictive control
Neural networks
Predictive control
quadrotor UAV
Robust control
robust H-infinity control
Simulation
Tracking
Turbulence models
Unmanned aerial vehicles
Unmanned helicopters
Wind
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Aimed at the stability problem of quadrotor Unmanned Aerial Vehicle (UAV) flight attitudes under random airflow disturbance conditions, a robust H-infinity-based dual cascade Model Predictive Control (MPC) attitude control method is proposed. Model Predictive Control itself has the capability to minimize the deviation between the prediction error and the control target by optimizing the control algorithm. The robust H-infinity controller can maintain stability in the face of system model uncertainty and external disturbances. The controller designed in this paper divides the attitude control loop into the following two parts: the angle loop and the angular velocity loop. The angle loop, serving as the main control loop of the attitude control, employs the robust H-infinity controller to process the angle of the quadrotor UAV and then transmits the processed value to the MPC controller. This approach reduces the computational load of the MPC controller. Simultaneously, by optimizing the algorithm, MPC minimizes the prediction error and the deviation from the control target. Simulation experiments confirm that the proposed algorithm improves the stability of the UAV attitude under random airflow disturbance conditions, while also achieving accurate tracking of the UAV’s position.
ISSN:2076-0825
2076-0825
DOI:10.3390/act13100392