Neuroadaptive Fault-Tolerant Control of Quadrotor UAVs: A More Affordable Solution

Neuroadaptive Fault-Tolerant Control of Quadrotor UAVs: A More Affordable Solution
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四旋翼无人机的神经自适应容错控制:更经济的解决方案

DOI:
10.1109/tnnls.2018.2876130
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发表时间:
2019-07
影响因子:
10.4
通讯作者:
Fu Jin
Fu Jin
中科院分区:
计算机科学1区
文献类型:
--
作者:
Song Yongduan;He Liu;Zhang Dong;Qian Jiye;Fu Jin

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研究了具有建模不确定性和执行器故障的四旋翼无人机的位置和姿态跟踪控制问题。一个全面的数学模型,反映了非线性和状态空间耦合的动态以及驱动故障和外部干扰。将径向基函数神经网络与虚拟参数估计算法相结合,提出了一种基于间接神经网络的自适应容错控制方法。与现有方法相比,该方法具有以下优点:1)不仅对非参数不确定性具有鲁棒性和自适应性,而且对意外的驱动故障具有容错能力;(2)不需要系统模型的精确信息就能保证稳定的跟踪;以及3)它仅涉及一个集总参数自适应,因此结构上更简单并且计算上更便宜,使得所得到的方案在编程方面要求较低,并且对于机载实现来说更可负担。通过计算机仿真验证了该方法的有效性和优越性。
This paper investigates the position and attitude tracking control problem of a quadrotor unmanned aerial vehicle subject to modeling uncertainties and actuator failures. A comprehensive mathematical model reflecting the nonlinearity and state-space coupling of the dynamics as well as actuation faults and external disturbances is derived. By combining the radial basis function neural networks (NNs) with virtual parameter estimating algorithms, an indirect NN-based adaptive fault-tolerant control scheme is developed, which exhibits several attractive features as compared with most existing methods: 1) it is not only robust and adaptive to nonparametric uncertainties but also tolerant to unexpected actuation faults; 2) it ensures stable tracking without the need for precise information on system model; and 3) it only involves one lumped parameter adaptation, thus is structurally simpler and computationally less expensive, rendering the resultant scheme less demanding in programming and more affordable for onboard implementation. The effectiveness and benefits of the proposed method are confirmed via computer simulation.
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