Supervisor:
Southwest Ordnance Industry Bureau
Organizer:
Chongqing Ordnance Industry Society
Chongqing University of Technology
Chongqing University of Technology
Data driven model predictive control based method for unmanned aerial vehicle trajectory tracking
| DOI: | 10.11809/bqzbgcxb2024.11.035 |
| Keywords: | data driven; online identification; model predictive control; fixed wing UAV; trajectory tracking; sparse identification |
| Abstract: | A new strategy is proposed for adaptive behavior control of fixed wing drones, aiming at enhancing performance of trajectory tracking tasks under complex and varied conditions.This method utilizes real time data and employs the online sparse recognition to construct and update a nonlinear dynamic system model.By applying this updated model to predictive controllers, it can swiftly adapt to varying circumstances.Flight test results show improved control performance through dynamic updates of the model parameters.In both windy and wind free conditions, the data driven predictive controller with online recognition can keep the drone’s flight path aligning closely with the target route.An empirical demonstration on a miniaturized onboard computer confirms that computational overhead of this method stays within a reasonable range, making it suitable for most edge computing platforms. |
| Published: | 2024-11-30 |