Adaptive Gain Scheduled Reentry Control for Reusable Launch Vehicles Based on Active Estimation and Classified Compensation
This study proposes an adaptive gain scheduled attitude control strategy for the reentry phase of reusable launch vehicles (RLVs), which is model‐independent and offers a convenient implementation. It originally integrates an extended state observer (ESO) with a sigmoid estimator to address the wide...
Saved in:
Published in | International journal of adaptive control and signal processing Vol. 39; no. 6; pp. 1274 - 1293 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Bognor Regis
Wiley Subscription Services, Inc
01.06.2025
|
Subjects | |
Online Access | Get full text |
ISSN | 0890-6327 1099-1115 |
DOI | 10.1002/acs.4006 |
Cover
Loading…
Summary: | This study proposes an adaptive gain scheduled attitude control strategy for the reentry phase of reusable launch vehicles (RLVs), which is model‐independent and offers a convenient implementation. It originally integrates an extended state observer (ESO) with a sigmoid estimator to address the wide envelope of velocities and altitudes, along with the strong nonlinearities and aerodynamic uncertainties. In particular, the ESO is used for disturbance estimation, whereas the sigmoid estimator is applied for disturbance classification. The proposed design achieves the adaptive online estimation and compensation of the RLV control gain and reduces the model dependence. In addition, the closed‐loop finite‐gain stability is investigated based on the Lyapunov theory, and the tracking boundedness can be proved. Finally, the effectiveness and robustness of the proposed design are verified based on numerical simulations and Monte Carlo tests, and the advantages of weak model dependence and strong ability on disturbance rejection are highlighted in the proposed design compared with the PID control and linear active disturbance rejection control. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0890-6327 1099-1115 |
DOI: | 10.1002/acs.4006 |