School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China.
China Aerodynamics Research and Development Center, Mianyang 621000, China.
Sensors (Basel). 2020 Apr 14;20(8):2213. doi: 10.3390/s20082213.
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'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.
远程控制无人作战飞行器（UCAV）将是未来空战的一种有前途的模式，它可以去除飞行员的身体限制，最大限度地发挥战斗机的性能，并有效地减少伤亡。然而，在这种模式下有两个困难：（1）飞行员-无线传感器-UCAV 的网络存在较大的时间延迟，这会降低飞行性能。（2）设计一种通用的预测方法对于远程控制不同的 UCAV 非常重要，即使 UCAV 的控制增稳系统的模型完全未知。考虑到这两个问题，本文提出了一种新的通用建模方法，并建立了一个通用的非线性不确定模型，该模型以飞行员的远程控制命令为输入，以带控制增稳系统的 UCAV 的状态为输出。为了处理模型的非线性不确定性，提出了一种神经网络观测器来在线识别非线性动力学模型。同时，为了保证整体观测器系统的稳定性，设计了一个自适应律来调整神经网络的权重。为了解决飞行员-无线传感器-UCAV 闭环系统中存在的较大传输时间延迟问题，基于所识别的非线性动力学模型设计了一个时变延迟状态预测器来预测延迟状态。此外，证明了整体观测器-预测器系统是一致最终有界的（UUB）。最后，通过两个仿真验证了所提出方法的有效性和通用性。结果表明，所提出的方法具有准确补偿时间延迟的良好性能，并且具有远程控制两种不同 UCAV 的通用性。
