Adaptive Neural Network Stochastic-Filter-Based Controller for Attitude Tracking With Disturbance Rejection
Adaptive Neural Network Stochastic-Filter-Based Controller for Attitude Tracking With Disturbance Rejection
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DOI：
10.1109/tnnls.2022.3183026
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发表时间：
2022-06
影响因子：
10.4
通讯作者：
Hashim A. Hashim-Hashim-A.-Hashim-36452482;K. Vamvoudakis
中科院分区：
文献类型：
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作者：
Hashim A. Hashim-Hashim-A.-Hashim-36452482;K. Vamvoudakis
本文提出了一种基于特殊正交群的李群的实时神经网络(NN)随机滤波控制器，作为姿态跟踪问题的新方法。介绍的解决方案由两部分组成：一个过滤器和一个控制器。首先，提出了一种基于神经网络的自适应随机滤波器，它直接利用星载传感器提供的观测值估计姿态分量和动态。滤波器设计考虑了姿态动力学固有的测量不确定性，即未知偏差和有损于角速度测量的噪声。基于神经网络的随机滤波器的闭环系统信号已被证明是半全局一致最终有界的。其次，结合所提出的估值器，提出了一种新的SO(3)控制律。控制律处理未知的扰动。此外，所提出的基于滤波的控制器的闭环信号被证明是SGUUB。该方法通过给出从低成本惯性测量单元中提取的数据来提供所需的控制信号，从而提供了稳健的跟踪性能。虽然基于滤波的控制器是以连续形式给出的，但也给出了离散实现。此外，还给出了该方法的单位四元数形式。在考虑低采样率、高初始化误差、高测量不确定性和未知干扰的情况下，利用离散形式证明了所提出的基于滤波的控制器的有效性和鲁棒性。
This article proposes a real-time neural network (NN) stochastic filter-based controller on the Lie group of the special orthogonal group SO(3) as a novel approach to the attitude tracking problem. The introduced solution consists of two parts: a filter and a controller. First, an adaptive NN-based stochastic filter is proposed, which estimates attitude components and dynamics using measurements supplied by onboard sensors directly. The filter design accounts for measurement uncertainties inherent to the attitude dynamics, namely, unknown bias and noise corrupting angular velocity measurements. The closed-loop signals of the proposed NN-based stochastic filter have been shown to be semiglobally uniformly ultimately bounded (SGUUB). Second, a novel control law on SO(3) coupled with the proposed estimator is presented. The control law addresses unknown disturbances. In addition, the closed-loop signals of the proposed filter-based controller have been shown to be SGUUB. The proposed approach offers robust tracking performance by supplying the required control signal given data extracted from low-cost inertial measurement units. While the filter-based controller is presented in continuous form, the discrete implementation is also presented. In addition, the unit-quaternion form of the proposed approach is given. The effectiveness and robustness of the proposed filter-based controller are demonstrated using its discrete form and considering low sampling rate, high initialization error, high level of measurement uncertainties, and unknown disturbances.