Abstract
Most existing control methods for quadrotor unmanned aerial vehicles (UAVs) are based on the primary assumption that the center of gravity (CoG) is fixed and is in the same position as the centroid, which is not necessarily true with swing load as continuously making CoG vary with the swing angle and substantially complicating the dynamic model of UAV. This article presents an adaptive learning and fault-tolerant control scheme for quadrotor UAVs with varying CoG and unknown moment of inertia. First, we establish the dynamic model of quadrotor UAVs in the presence of time-varying CoG, input saturation, and actuator fault. Then, we design a fault-tolerant adaptive learning controller for the quadrotor UAVs and show that both linear and angular velocity tracking errors are ensured to converge to a residual set around zero in the presence of full-state constraints. Furthermore, all signals in the closed-loop system are uniformly ultimately bounded. Simulation studies also confirm the effectiveness of the proposed control method.
| Original language | English |
|---|---|
| Pages (from-to) | 5610-5622 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 32 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 1 Dec 2021 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2012 IEEE.
Funding
This work was supporeted in part by the Zhejiang Laboratory under Grant 2019NB0AB06, in part by the Fundamental Research Funds for the Central Universities under Grant 2019CDCGZDH335, and in part by the National Natural Science Foundation under Grant 61833013, Grant 61773081, Grant 61860206008, and Grant 61933012
Keywords
- Adaptive learning
- center of gravity (CoG)
- full-state constraints
- input saturation
- moment of inertia