vault backup: 2025-05-12 11:19:21
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.obsidian/plugins/colored-tags/data.json
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@ -232,7 +232,29 @@
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"distributed-systems": 220,
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"PRET-machines": 221,
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"real-time-systems": 222,
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"time-synchronization": 223
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"time-synchronization": 223,
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"Computer-networks": 224,
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"Concurrent-computing": 225,
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"Convergence-of-numerical-methods": 226,
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"Electrical-equipment-industry": 227,
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"Industrial-control": 228,
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"Proposals": 229,
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"Taxonomy": 230,
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"Neural-networks-Computer-science": 231,
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"Nonlinear-control-theory": 232,
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"General": 233,
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"Adaptive-control": 234,
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"Control": 235,
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"artificial-intelligence": 236,
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"complexity": 237,
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"Control-Engineering": 238,
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"development": 239,
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"genetic-algorithms": 240,
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"Identification": 241,
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"model": 242,
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"Modelling": 243,
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"Nonlinear-control": 244,
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"Wavelets": 245
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},
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"_version": 3
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}
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@ -0,0 +1,62 @@
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---
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authors:
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- "Agarwal, M."
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citekey: "agarwalSystematicClassificationNeuralnetworkbased1997"
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publish_date: 1997-04-01
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journal: "IEEE Control Systems Magazine"
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volume: 17
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issue: 2
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pages: 75-93
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last_import: 2025-05-12
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---
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# Indexing Information
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Published: 1997-04
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**DOI**
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[10.1109/37.581297](https://doi.org/10.1109/37.581297)
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#Control-systems, #Stability-analysis, #Computer-networks, #Concurrent-computing, #Convergence-of-numerical-methods, #Electrical-equipment-industry, #Industrial-control, #Neural-networks, #Proposals, #Taxonomy
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#ToRead
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>[!Abstract]
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>Successful industrial applications and favorable comparisons with conventional alternatives have motivated the development of a large number of schemes for neural-network-based control. Each scheme is usually composed of several independent functional features, which makes it difficult to identify precisely what is new in the scheme. Help from available overviews is therefore often inadequate, since they usually discuss only the most important overall schemes. This work breaks the available schemes down to their essential functional features and organizes the latter into a multi-level classification. The classification reveals that similar schemes often get placed in different categories, fundamentally different features often get lumped into a single category, and proposed new schemes are often merely permutations and combinations of the well-established fundamental features. The classification has two main sections: neural network only as an aid; and neural network as controller.>[!seealso] Related Papers
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>
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# Annotations
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## Notes
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![[Paper Notes/A systematic classification of neural-network-based control.md]]
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## Highlights From Zotero
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>[!highlight] Highlight
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> rigorous comparisons neural-network controllers have fared better than well- established conventional options when the plant characteristics are poorly known [2-61.
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> 2025-04-15 9:18 am
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>[!done] Important
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> In order to illus- tr te the unavoidable basic terminology for the unfamiliar re 9 der, a neural network can be regarded simply as a generic
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> 2025-04-15 9:22 am
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>[!highlight] Highlight
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> mapping,
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> 2025-04-17 4:06 pm
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>[!highlight] Highlight
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> d also for classifi- cation and optimization tasks. An overview of the proposed classification is shown in Fig. 1. The relatively limited option of using neural networks to merely aid a non-neural controller is further classified in the following section. Of the schemes in which the con
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> 2025-04-17 4:06 pm
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>[!highlight] Highlight
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> Of the schemes in which the controller itself is a neural network, the section “Train Based on U” classifies the alternative where control-input signals U are available for training the neural controller and the section “Train Based on Goal”classifies the option where the network devises the needed control strategy on its own, based on the ultimate control objective. C
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> 2025-04-17 1:02 pm
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## Follow-Ups
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---
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authors:
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- "Poznyak, Alexander S."
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- "Yu, Wen"
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- "Sanchez, Edgar N."
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citekey: "poznyakDifferentialNeuralNetworks2001"
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publish_date: 2001-01-01
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publisher: "World Scientific"
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location: "Singapore ;"
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last_import: 2025-05-12
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---
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# Indexing Information
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Published: 2001-01
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**ISBN**
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[9786611956738](https://www.isbnsearch.org/isbn/9786611956738)
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#Neural-networks-Computer-science, #Nonlinear-control-theory
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#ToRead
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>[!Abstract]
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>This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its ap>[!seealso] Related Papers
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>
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# Annotations
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## Notes
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![[Paper Notes/Differential neural networks for robust nonlinear control- identification, state estimation and trajectory tracking.md]]
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## Highlights From Zotero
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## Follow-Ups
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---
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authors:
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- "Emami, Seyyed Ali"
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- "Castaldi, Paolo"
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- "Banazadeh, Afshin"
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citekey: "emamiNeuralNetworkbasedFlight2022"
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publish_date: 2022-01-01
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journal: "Annual Reviews in Control"
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volume: 53
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pages: 97-137
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last_import: 2025-05-12
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---
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# Indexing Information
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Published: 2022-01
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**DOI**
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[10.1016/j.arcontrol.2022.04.006](https://doi.org/10.1016/j.arcontrol.2022.04.006)
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#Neural-networks, #Flight-control, #Intelligent-control, #Reinforcement-learning
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#ToRead
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>[!Abstract]
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>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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>
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# Annotations
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## Notes
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![[Paper Notes/Neural network-based flight control systems- Present and future.md]]
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## Highlights From Zotero
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>[!highlight] Highlight
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> 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]
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> 2025-04-07 12:54 pm
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## Follow-Ups
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38
Literature Notes/Nonlinear Identification and Control.md
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38
Literature Notes/Nonlinear Identification and Control.md
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---
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authors:
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- "Liu, G. P."
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- "Grimble, Michael J."
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- "Johnson, Michael A."
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citekey: "liuNonlinearIdentificationControl2001"
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publish_date: 2001-01-01
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publisher: "Springer"
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location: "London"
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last_import: 2025-05-12
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---
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# Indexing Information
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Published: 2001-01
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**ISBN**
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[978-1-4471-1076-7 978-1-4471-0345-5](https://www.isbnsearch.org/isbn/978-1-4471-1076-7 978-1-4471-0345-5)
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#Adaptive-control, #Control, #artificial-intelligence, #complexity, #Control-Engineering, #development, #genetic-algorithms, #Identification, #learning, #model, #Modelling, #Neural-Networks, #Nonlinear-control, #Wavelets
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#ToRead
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>[!seealso] Related Papers
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>
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# Annotations
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## Notes
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![[Paper Notes/Nonlinear Identification and Control.md]]
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## Highlights From Zotero
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## Follow-Ups
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---
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authors:
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- "Carr, Steven"
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- "Jansen, Nils"
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- "Junges, Sebastian"
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- "Topcu, Ufuk"
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citekey: "carrSafeReinforcementLearning2023"
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publish_date: 2023-06-26
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journal: "Proceedings of the AAAI Conference on Artificial Intelligence"
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volume: 37
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issue: 12
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pages: 14748-14756
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last_import: 2025-05-12
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---
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# Indexing Information
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Published: 2023-06
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**DOI**
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[10.1609/aaai.v37i12.26723](https://doi.org/10.1609/aaai.v37i12.26723)
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#General
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#ToRead
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>[!Abstract]
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>Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment. A family of approaches to this problem assume domain knowledge in the form of a (partial) model of this environment to decide upon the safety of an action. A so-called shield forces the RL agent to select only safe actions. However, for adoption in various applications, one must look beyond enforcing safety and also ensure the applicability of RL with good performance. We extend the applicability of shields via tight integration with state-of-the-art deep RL, and provide an extensive, empirical study in challenging, sparse-reward environments under partial observability. We show that a carefully integrated shield ensures safety and can improve the convergence rate and final performance of RL agents. We furthermore show that a shield can be used to bootstrap state-of-the-art RL agents: they remain safe after initial learning in a shielded setting, allowing us to disable a potentially too conservative shield eventually.>[!seealso] Related Papers
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>
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# Annotations
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## Notes
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![[Paper Notes/Safe Reinforcement Learning via Shielding under Partial Observability.md]]
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## Highlights From Zotero
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## Follow-Ups
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---
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authors:
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- "Alshiekh, Mohammed"
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- "Bloem, Roderick"
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- "Ehlers, Rüdiger"
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- "Könighofer, Bettina"
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- "Niekum, Scott"
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- "Topcu, Ufuk"
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citekey: "alshiekhSafeReinforcementLearning2018"
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publish_date: 2018-04-29
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journal: "Proceedings of the AAAI Conference on Artificial Intelligence"
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volume: 32
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issue: 1
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last_import: 2025-05-12
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---
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# Indexing Information
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Published: 2018-04
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**DOI**
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[10.1609/aaai.v32i1.11797](https://doi.org/10.1609/aaai.v32i1.11797)
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#Formal-Methods
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#ToRead
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>[!Abstract]
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>Reinforcement learning algorithms discover policies that maximize reward, but do not necessarily guarantee safety during learning or execution phases. We introduce a new approach to learn optimal policies while enforcing properties expressed in temporal logic. To this end, given the temporal logic specification that is to be obeyed by the learning system, we propose to synthesize a reactive system called a shield. The shield monitors the actions from the learner and corrects them only if the chosen action causes a violation of the specification. We discuss which requirements a shield must meet to preserve the convergence guarantees of the learner. Finally, we demonstrate the versatility of our approach on several challenging reinforcement learning scenarios.>[!seealso] Related Papers
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>
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# Annotations
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## Notes
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![[Paper Notes/Safe Reinforcement Learning via Shielding.md]]
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## Highlights From Zotero
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## Follow-Ups
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