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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jie, L. Kai, L. Hang, X. Li, L. |
| Copyright Year | 2008 |
| Description | Author affiliation: The First Aeronautical Institute of Air Force (Jie, L.; Kai, L.; Hang, X.; Li, L.) |
| Abstract | This paper introduces the control strategy and method of non-linear dynamic inversion. Depending on the thought of dynamic inversion, the mathematical model of super-maneuverable aircraft is set up and the simulation curves are produced. Through analyzing the curves, we can draw out the feasibility and limitation of non-linear dynamic inversion.This paper introduces below contents :the feasibility of dynamic inversion in super-maneuverable flight; the modeling error and inversion error; the influence on super-maneuverable flight caused by uncertain factors. To solve above problems, the paper points out two kinds of methods. One is fuzzy self-adaptive technology. It describes the characteristic of fuzzy controller and points out its limitation that it can¿t be used to eliminate the error caused by uncertain factors. The other is neural network technology. It describes the dynamic inversion off-line neural network controller. On-line neural network controller eliminates error. At last the conclusion is gotten that neural network is more advantageous and the development direction of super-maneuverable flight. |
| Starting Page | 1185 |
| Ending Page | 1188 |
| File Size | 508604 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424416752 |
| DOI | 10.1109/RAMECH.2008.4690876 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-09-21 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Aerospace control Aerodynamics Nonlinear dynamical systems Neural networks State feedback Nonlinear equations Mathematical model Aircraft propulsion Fuzzy control Error correction fuzzy self-adaptive, neural network Super-maneuverable flight, dynamic inversion |
| Content Type | Text |
| Resource Type | Article |
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