Neural-Network-Based Adaptive Finite-Time Output Feedback Control for Spacecraft Attitude Tracking
This brief is concerned with neural network (NN)-based adaptive finite-time output feedback attitude tracking control for rigid spacecraft in the presence of actuator saturation, inertial uncertainty, and external disturbance. First, a neural state observer is designed to estimate the unknown state....
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| Vydáno v: | IEEE transaction on neural networks and learning systems Ročník 34; číslo 10; s. 8116 - 8123 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
IEEE
01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | This brief is concerned with neural network (NN)-based adaptive finite-time output feedback attitude tracking control for rigid spacecraft in the presence of actuator saturation, inertial uncertainty, and external disturbance. First, a neural state observer is designed to estimate the unknown state. Then, based on the estimated state, the adaptive neural finite-time command filtered backstepping (CFB) is applied to construct virtual control signal and controller with updating law. The finite-time command filter is given to avoid the computation complexity problem in traditional backstepping, and the compensation signals based on fractional power are constructed to remove filtering errors. Using Lyapunov stability theory, we show that the attitude tracking error (TE) can converge into the desired neighborhood of the origin in finite time and all the signals in the closed-loop system are bounded in finite time although input saturation exists. The numerical simulations are used to show the effectiveness of the given algorithm. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 2162-237X 2162-2388 2162-2388 |
| DOI: | 10.1109/TNNLS.2022.3144493 |