Zhou Ning, Cheng Xiaodong, Xia Yuanqing, Liu Yanjun
IEEE Trans Cybern. 2022 Jan;52(1):344-356. doi: 10.1109/TCYB.2020.2969281. Epub 2022 Jan 11.
This article investigates the attitude stabilization problem of a rigid spacecraft with actuator saturation and failures. Two neural network-based control schemes are proposed using anti-saturation adaptive strategies. To satisfy the input constraint, we design two controllers in a saturation function structure. Taking into account the modeling uncertainties, external disturbances, and adverse effects from actuator faults and failures, the first anti-saturation adaptive controller is implemented based on radial basis function neural networks (RBFNNs) with a fixed-time terminal sliding mode (FTTSM) containing a tunable parameter. Then, we upgrade the proposed controller to a fully adaptive-gain anti-saturation version, in order to strengthen the robustness and adaptivity with respect to actuator faults and failures, unknown mass properties, and external disturbances. In the two schemes, all of the designed adaptive parameters are scalars, thus they only require light computational load and can avoid the redesign process of the controller during spacecraft operation. Finally, the feasibility of the proposed methods is illustrated via two numerical examples.
本文研究了具有执行器饱和与故障的刚性航天器姿态稳定问题。提出了两种基于神经网络的控制方案，并采用了抗饱和自适应策略。为满足输入约束，我们设计了两种饱和函数结构的控制器。考虑到建模不确定性、外部干扰以及执行器故障和失效的不利影响，基于具有可调参数的固定时间终端滑模（FTTSM）的径向基函数神经网络（RBFNN）实现了第一种抗饱和自适应控制器。然后，我们将所提出的控制器升级为完全自适应增益抗饱和版本，以增强对执行器故障和失效、未知质量特性以及外部干扰的鲁棒性和适应性。在这两种方案中，所有设计的自适应参数都是标量，因此它们只需要轻量级的计算负载，并且可以避免航天器运行期间控制器的重新设计过程。最后，通过两个数值例子说明了所提方法的可行性。
