vault backup: 2025-04-07 12:15:45

201 Metadata/My Library.bib

@@ -2498,6 +2498,24 @@ Artificial Intelligence Program.pdf}
   file = {/home/danesabo/Zotero/storage/H2IWZM6I/Ellison et al. - Extending AADL for Security Design Assurance of Cy.pdf}
 }
 
+@article{emamiNeuralNetworkbasedFlight2022,
+  title = {Neural Network-Based Flight Control Systems: {{Present}} and Future},
+  shorttitle = {Neural Network-Based Flight Control Systems},
+  author = {Emami, Seyyed Ali and Castaldi, Paolo and Banazadeh, Afshin},
+  date = {2022-01-01},
+  journaltitle = {Annual Reviews in Control},
+  shortjournal = {Annual Reviews in Control},
+  volume = {53},
+  pages = {97--137},
+  issn = {1367-5788},
+  doi = {10.1016/j.arcontrol.2022.04.006},
+  url = {https://www.sciencedirect.com/science/article/pii/S1367578822000219},
+  urldate = {2025-04-07},
+  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.},
+  keywords = {Flight control,Intelligent control,Neural networks,Reinforcement learning},
+  file = {/home/danesabo/Zotero/storage/JVE6ZH28/Emami et al. - 2022 - Neural network-based flight control systems Present and future.pdf;/home/danesabo/Zotero/storage/TJUGEB38/S1367578822000219.html}
+}
+
 @book{EnhancingEffectivenessTeam2015,
   title = {Enhancing the {{Effectiveness}} of {{Team Science}}},
   date = {2015-07-15},
@@ -7857,6 +7875,24 @@ Subject\_term: Careers, Politics, Policy},
   file = {/home/danesabo/Zotero/storage/FD3USCZJ/Wang et al. - 2017 - Adaptive Critic Nonlinear Robust Control A Survey.pdf}
 }
 
+@article{wangAdaptiveCriticNonlinear2017a,
+  title = {Adaptive {{Critic Nonlinear Robust Control}}: {{A Survey}}},
+  shorttitle = {Adaptive {{Critic Nonlinear Robust Control}}},
+  author = {Wang, Ding and He, Haibo and Liu, Derong},
+  date = {2017-10},
+  journaltitle = {IEEE Transactions on Cybernetics},
+  volume = {47},
+  number = {10},
+  pages = {3429--3451},
+  issn = {2168-2275},
+  doi = {10.1109/TCYB.2017.2712188},
+  url = {https://ieeexplore.ieee.org/abstract/document/7967695/},
+  urldate = {2025-04-07},
+  abstract = {Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.},
+  keywords = {Adaptive critic designs,adaptive/approximate dynamic programming (ADP),boundedness,convergence,Dynamic programming,Learning (artificial intelligence),neural networks,Nonlinear systems,optimal control,Optimal control,reinforcement learning,robust control,Robust control,Robustness,stability,Uncertainty},
+  file = {/home/danesabo/Zotero/storage/CMHWUSWG/Wang et al. - 2017 - Adaptive Critic Nonlinear Robust Control A Survey.pdf}
+}
+
 @inproceedings{wangDiffuseBotBreedingSoft2023,
   title = {{{DiffuseBot}}: {{Breeding Soft Robots With Physics-Augmented Generative Diffusion Models}}},
   booktitle = {Advances in {{Neural Information Processing Systems}}},