Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network
| Autor: | Yonghua Fan, Bin Xu, Guicai Fang, Jie Yan, Hongyang Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Observer (quantum physics) Artificial neural network Computer science 02 engineering and technology lcsh:Chemical technology time delay Biochemistry Article Atomic and Molecular Physics and Optics Analytical Chemistry state predictor Nonlinear system 020901 industrial engineering & automation unmanned combat aerial vehicle (UCAV) neural network observer Control theory Bounded function wireless sensor network (WSN) 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 020201 artificial intelligence & image processing Electrical and Electronic Engineering Instrumentation Wireless sensor network |
| Zdroj: | Sensors Volume 20 Issue 8 Sensors, Vol 20, Iss 2213, p 2213 (2020) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s20082213 |
| Popis: | Remotely piloted unmanned combat aerial vehicle (UCAV) will be a prospective mode of air fight in the future, which can remove the physical restraint of the pilot, maximize the performance of the fighter and effectively reduce casualties. However, it has two difficulties in this mode: (1) There is greater time delay in the network of pilot-wireless sensor-UCAV, which can degrade the piloting performance. (2) Designing of a universal predictive method is very important to pilot different UCAVs remotely, even if the model of the control augmentation system of the UCAV is totally unknown. Considering these two issues, this paper proposes a novel universal modeling method, and establishes a universal nonlinear uncertain model which uses the pilot&rsquo s remotely piloted command as input and the states of the UCAV with a control augmentation system as output. To deal with the nonlinear uncertainty of the model, a neural network observer is proposed to identify the nonlinear dynamics model online. Meanwhile, to guarantee the stability of the overall observer system, an adaptive law is designed to adjust the neural network weights. To solve the greater transmission time delay existing in the pilot-wireless sensor-UCAV closed-loop system, a time-varying delay state predictor is designed based on the identified nonlinear dynamics model to predict the time delay states. Moreover, the overall observer-predictor system is proved to be uniformly ultimately bounded (UUB). Finally, two simulations verify the effectiveness and universality of the proposed method. The results indicate that the proposed method has desirable performance of accurately compensating the time delay and has universality of remotely piloting two different UCAVs. |
| Databáze: | OpenAIRE |
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