3.3 KiB
| authors | citekey | publish_date | journal | volume | pages | last_import | |||
|---|---|---|---|---|---|---|---|---|---|
|
emamiNeuralNetworkbasedFlight2022 | 2022-01-01 | Annual Reviews in Control | 53 | 97-137 | 2025-05-12 |
Indexing Information
Published: 2022-01
DOI 10.1016/j.arcontrol.2022.04.006 #Neural-networks, #Flight-control, #Intelligent-control, #Reinforcement-learning
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[!Abstract] 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. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.>[!seealso] Related Papers
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Notes
!Paper Notes/Neural network-based flight control systems- Present and future.md
Highlights From Zotero
[!highlight] Highlight Although the words Intelligence and Autonomy have been widely employed interchangeably, there is an essential conceptual difference between them [1]. Different definitions have been given for both concepts in the literature [2, 3]. However, in a general view, the intelligence may be defined as a very general mental capability that involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience [3]. On the other hand, the ability to generate one’s own purposes without any instruction from outside can be interpreted as the autonomy of a system [1] 2025-04-07 12:54 pm