基于神经网络的飞行控制系统：现状与未来
Neural Network-based Flight Control Systems: Present and Future
摘要：
作为这一领域的第一篇综述，本文对智能飞行控制系统（IFCS），特别是基于人工神经网络的IFCS进行了深入的数学分析。IFCS在过去二十年中在方法和技术方面的快速发展需要对其进行全面的审视，以更好地展示当前阶段和开发真正智能飞行管理单元的关键剩余步骤。为此，在本文中，我们将提供一个详细的基于神经网络（NN）的飞行控制系统的数学视图和仍然存在的挑战性问题。该文件将涵盖基于模型和无模型的综合融资战略。基于模型的方法包括基本的反馈误差学习方案，伪控制策略，和神经反推方法。此外，不同的方法来分析闭环稳定性的IFCS，他们的要求，和他们的局限性将进行详细讨论。还将包括可与基本化安论坛相结合的各种补充功能，例如容错能力、对系统制约因素的考虑、以及将神经网络与干扰观测器等其他稳健和自适应要素相结合。另一方面，关于无模型飞行控制器，间接和直接自适应控制系统，包括使用基于NN的系统辨识的间接自适应控制，使用NN的近似动态规划，以及基于强化学习的自适应最优控制将被仔细地讨论。最后，通过对政府间化学品安全论坛目前的发展阶段提出一个条理清晰的观点，彻底查明了今后必须加以解决的具有挑战性的问题。
英文摘要：
As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
