This paper presents a fault-tolerant aircraft control (FTAC) scheme against actuator faults. First, the upper bounds of the norms of the unknown functions are introduced, which contain actuator faults and model uncertainties. Subsequently, self-constructing fuzzy neural networks (SCFNNs) with adaptive laws are capable of obtaining the bounds. The bound estimation can reduce the computational burden with a lower amount of rules and weights, rather than the dynamic matrix approximation. Moreover, with the aid of SCFNNs, a multivariable sliding mode control (SMC) is developed to guarantee the finite-time stability of the handicapped aircraft. As compared to the existing intelligent FTAC techniques, the proposed method has twofold merits: fault accommodation can be promptly accomplished and decoupled difficulties can be overcome. Finally, simulation results from the nonlinear longitudinal Boeing 747 aircraft model illustrate the capability of the presented FTAC scheme.

中文翻译：

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执行器故障下基于自建模糊神经网络和多变量SMC的飞机容错控制


本文提出了一种针对执行器故障的容错飞机控制（FTAC）方案。首先，引入未知函数范数的上限，其中包含执行器故障和模型不确定性。随后，具有自适应律的自构建模糊神经网络（SCFNN）能够获得边界。与动态矩阵近似相比，边界估计可以通过较少数量的规则和权重来减少计算负担。此外，在SCFNN的帮助下，开发了多变量滑模控制（SMC）来保证残障飞机的有限时间稳定性。与现有的智能FTAC技术相比，该方法具有双重优点：能够及时完成故障适应并克服解耦困难。最后，非线性纵向波音 747 飞机模型的仿真结果说明了所提出的 FTAC 方案的能力。