LAPSE:2023.17491
Published Article
LAPSE:2023.17491
Parameterized Trajectory Optimization and Tracking Control of High Altitude Parafoil Generation
Xinyu Long, Mingwei Sun, Minnan Piao, Zengqiang Chen
March 6, 2023
Abstract
Parafoil trajectory directly affects the power generation of a high-altitude wind power generation (HAWPG) device. Therefore, it is particularly important to optimize the parafoil trajectory and then to track it effectively. In this paper, the trajectory of the parafoil at high altitudes is optimized and tracked in a comprehensively parameterized manner. Both the complex dynamic characteristics of the parafoil and the dexterous demand of the high-altitude controller are considered. Firstly, the trajectory variables and control signals are parameterized as Lagrange polynomials in terms of the corresponding values at the selected nodes. Then, the Radau pseudospectral method (PSM) is employed to reformulate the original dynamic trajectory optimization problem into a static nonlinear programming (NLP) problem. By doing so, the parameterized optimal trajectory, which has the maximum net power generation, can be obtained. To attenuate the strong nonlinear, multivariable and coupling characteristics of the flexible parafoil, a bandwidth parameterized linear extended state observer (ESO) is used to estimate and reject these dynamics explicitly in a unified way. Finally, the simulation results demonstrate the effectiveness of the proposed parameterized trajectory optimization and control strategies. The main contribution of this study is that complicated nonlinear parafoil dynamics with a complex trajectory can be well regulated by a PID-type linear time-invariant controller, which is appealing for practitioners.
Keywords
extended state observer (ESO), nonlinear programming (NLP), pseudospectral method (PSM), trajectory optimization
Suggested Citation
Long X, Sun M, Piao M, Chen Z. Parameterized Trajectory Optimization and Tracking Control of High Altitude Parafoil Generation. (2023). LAPSE:2023.17491
Author Affiliations
Long X: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Sun M: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Piao M: College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Chen Z: College of Artificial Intelligence, Nankai University, Tianjin 300350, China [ORCID]
Journal Name
Energies
Volume
14
Issue
22
First Page
7460
Year
2021
Publication Date
2021-11-09
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14227460, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.17491
This Record
External Link

https://doi.org/10.3390/en14227460
Publisher Version
Download
Files
Mar 6, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
218
Version History
[v1] (Original Submission)
Mar 6, 2023
 
Verified by curator on
Mar 6, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.17491
 
Record Owner
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version