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Auswahl der wissenschaftlichen Literatur zum Thema „Control Barrier Function“
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Zeitschriftenartikel zum Thema "Control Barrier Function"
NAKAMURA, Hisakazu, Takao YOSHINAGA, Yu KOYAMA und Jun ETOH. „Control Barrier Function Based Human Assist Control“. Transactions of the Society of Instrument and Control Engineers 55, Nr. 5 (2019): 353–61. http://dx.doi.org/10.9746/sicetr.55.353.
Der volle Inhalt der QuelleWills, Adrian G., und William P. Heath. „Barrier function based model predictive control“. Automatica 40, Nr. 8 (August 2004): 1415–22. http://dx.doi.org/10.1016/j.automatica.2004.03.002.
Der volle Inhalt der QuelleHAYASHI, Yuka, und Hisakazu NAKAMURA. „Control Barrier Function Based Human Assist Control under Disturbance“. Transactions of the Society of Instrument and Control Engineers 57, Nr. 8 (2021): 339–48. http://dx.doi.org/10.9746/sicetr.57.339.
Der volle Inhalt der QuelleHayashi, Y., M. Igarashi und H. Nakamura. „Contact Assist Control Barrier Function for Human Assist Control“. IFAC-PapersOnLine 52, Nr. 16 (2019): 741–46. http://dx.doi.org/10.1016/j.ifacol.2019.12.051.
Der volle Inhalt der QuelleObeid, Hussein, Leonid M. Fridman, Salah Laghrouche und Mohamed Harmouche. „Barrier function-based adaptive sliding mode control“. Automatica 93 (Juli 2018): 540–44. http://dx.doi.org/10.1016/j.automatica.2018.03.078.
Der volle Inhalt der QuelleTezuka, Issei, und Hisakazu Nakamura. „Strict Zeroing Control Barrier Function for Continuous Safety Assist Control“. IEEE Control Systems Letters 6 (2022): 2108–13. http://dx.doi.org/10.1109/lcsys.2021.3138526.
Der volle Inhalt der QuelleLi, Boqian, Shiping Wen, Zheng Yan, Guanghui Wen und Tingwen Huang. „A Survey on the Control Lyapunov Function and Control Barrier Function for Nonlinear-Affine Control Systems“. IEEE/CAA Journal of Automatica Sinica 10, Nr. 3 (März 2023): 584–602. http://dx.doi.org/10.1109/jas.2023.123075.
Der volle Inhalt der QuelleWang, Jian, He He und Jiafeng Yu. „Stabilization with guaranteed safety using Barrier Function and Control Lyapunov Function“. Journal of the Franklin Institute 357, Nr. 15 (Oktober 2020): 10472–91. http://dx.doi.org/10.1016/j.jfranklin.2020.08.026.
Der volle Inhalt der QuelleHAYASHI, Yuka, und Hisakazu NAKAMURA. „Human Assist Control of Electric Wheelchair by Using Control Barrier Function“. Transactions of the Society of Instrument and Control Engineers 56, Nr. 3 (2020): 132–40. http://dx.doi.org/10.9746/sicetr.56.132.
Der volle Inhalt der QuelleIGARASHI, Motoi, Maki TAKAI und Hisakazu NAKAMURA. „Control Barrier Function Based Human Assist Control for Moving Obstacle Avoidance“. Transactions of the Society of Instrument and Control Engineers 56, Nr. 9 (2020): 432–41. http://dx.doi.org/10.9746/sicetr.56.432.
Der volle Inhalt der QuelleDissertationen zum Thema "Control Barrier Function"
Wu, Guofan. „Safety-critical Geometric Control Design with Application to Aerial Transportation“. Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1108.
Der volle Inhalt der QuelleOttoson, Jakob. „Comparative analysis of pathogen occurrence in wastewater : management strategies for barrier function and microbial control“. Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233.
Der volle Inhalt der QuelleKanso, Soha. „Contributions to Safe Reinforcement Learning and Degradation Tolerant Control Design“. Electronic Thesis or Diss., Université de Lorraine, 2024. https://docnum.univ-lorraine.fr/ulprive/DDOC_T_2024_0261_KANSO.pdf.
Der volle Inhalt der QuelleSafety-critical dynamical systems are essential in various industries, such as aerospace domain, autonomous systems, robots in healthcare area etc. where safety issues and structural or functional failure may lead to catastrophic consequences. A significant challenge in these systems is the degradation of components and actuators, which can compromise safety and stability of systems. As such, incorporating state of system's health within the control design framework is essential to ensure tolerance to functional degradation. Moreover, such system models often involve uncertainties and incomplete knowledge, especially as components degrade, altering system dynamics in a nonlinear manner, calling for development of learning approaches that envisage assimilation of available data within the control learning paradigm. However, assuring safety during the learning phase (exploration) as well as operational phase (exploitation) is of paramount importance when it comes to such dynamical systems. Traditional model-based control approaches, require precise system models, making them less effective under these conditions. In this context, Reinforcement Learning (RL) emerges as a powerful approach, capable of learning optimal control laws for partially or fully unknown dynamic systems, in the presence of input-output data (without the exact knowledge of system models). However, development and implementation of RL based approaches present their own challenges: the exploration phase, necessary for learning, can lead the system into unsafe regions and accelerate the speed of degradation; further, provable safety guarantees during the operational (exploitation) phase are equally important to ensure safety throughout the system operation. In this context, Safe Reinforcement Learning (Safe RL) paradigm targets development of RL based approaches that prioritize the safety guarantees, along with traditional stability, and optimality of systems. This thesis addresses these challenges by developing novel control learning strategies that adapt to system uncertainties and functional degradation. The main contributions of this thesis lie in proposition of novel approaches to addressing the challenges of system safety and stability, as well as decelerating the speed of degradation, thereby advancing the fields of safe RL and leading to proposition of Degradation-Tolerant Control (DTC). These contributions include:• ensuring the optimality, safety, and stability of control policy during both exploration and exploitation phases of RL. By integrating Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) within the RL framework, safe exploration and stable operation are ensured for both regulation and tracking problems. CBFs are used to define safe operating regions, while CLFs ensure that the system remains stable. These functions are incorporated into the RL algorithms to guide the learning process, ensuring that safety and stability constraints are respected;• decelerating the speed of degradation by incorporating degradation rates into control design, initially employing an optimal control approach in discrete time for linear systems. This ensures that control actions minimize the speed of degradation on system components, thereby extending their lifespan. For nonlinear systems, RL methods are employed to address the problem in both discrete and continuous time, providing adaptable solutions to complex dynamics;• proposal of a novel cyclic RL algorithm to ensure system stability under actuator degradation. This algorithm cyclically updates the learned control law, ensuring proper adaptation as system components degrade. The cyclic nature of the algorithm allows for reassessment and adjustment of control policies, ensuring continuous optimal performance despite ongoing degradation. These developed approaches were implemented through simulations, demonstrating their effectiveness in academic applications
Tan, Xiao. „Partitioning and Control for Dynamical Systems Evolving on Manifolds“. Licentiate thesis, KTH, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283672.
Der volle Inhalt der QuelleQC 20201012
Zeferino, Cristiane Lionço. „Avaliação e controle de margem de carregamento em sistemas elétricos de potência“. Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-05052011-091651/.
Der volle Inhalt der QuelleThis work proposes the determination of the Maximum Loading Point (MLP) in electric power system via Lagrangian Modified Barrier Function (LMBF) method, a variant of Interior Point (IP). The LMBF method is also used to determine which bus, for each system, has the highest sensitivity of load factor, i.e., which bus would be the first to have load shedding in order to increase the loading margin system and thus prevent voltage collapse. To validate this approach, the Sensitivity Analysis (SA) technique was used for the confirmation of the results obtained by the LMBF method. The formulation of the problem considered the equations of power balance of the electrical system equality constraints, and the buses voltage magnitude limits, as well as the limits of reactive power control at the buses of that power inequality constraints. Case studies were conducted in a system of 3 buses and IEEE systems 14, 57, 118 and 300 buses, demonstrating the robustness and efficiency of the proposed algorithms.
Carli, Nicola de. „Active perception and localization for multi-robot systems“. Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS013.
Der volle Inhalt der QuelleIn this thesis, we tackle challenges in the localization of multi-robot systems, focusing on cooperative localization in non-infinitesimally rigid formations with sensing constraints. Our contributions introduce a framework in which the possibly conflicting goals of connectivity maintenance, task execution and the information acquisition are "mediated" using a quadratic program and the control barrier functions and control Lyapunov function formalism. Another contribution of this thesis addresses distributed active localization of multiple moving targets by a group of flying robots using camera-based measurements, while accommodating other tasks if system redundancy permits. Also in this case, the problem formulation utilizes a quadratic program and control barrier functions. Building on the control barrier function and quadratic program framework, we identify and address limitations in the existing state of the art, particularly in distributed control barrier functions. Our modifications result in a controller that converges to the centralized optimal solution. Lastly, we present an observer methodology as a novel contribution, allowing cooperative localization of a multi-robot system in a common frame using body-frame relative measurements
Zhang, Nan. „SCALE MODELS OF ACOUSTIC SCATTERING PROBLEMS INCLUDING BARRIERS AND SOUND ABSORPTION“. UKnowledge, 2018. https://uknowledge.uky.edu/me_etds/119.
Der volle Inhalt der QuelleLage, Guilherme Guimarães. „O fluxo de potência ótimo reativo com variáveis de controle discretas e restrições de atuação de dispositivos de controle de tensão“. Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-29042013-114259/.
Der volle Inhalt der QuelleThis work proposes a novel model and a new approach for solving the reactive optimal power flow problem with discrete control variables and voltage-control actuation constraints. Mathematically, such problem is formulated as a nonlinear programming problem with continuous and discrete variables and complementarity constraints, whose proposed resolution approach is based on solving a sequence of modified problems by the discrete penalty-modified barrier Lagrangian function algorithm. In this approach, the original problem is modified in the following way: 1) the discrete variables are treated as continuous by sinusoidal functions incorporated into the objective function of the original problem; 2) the complementarity constraints are transformed into equivalent inequality constraints; and 3) the inequality constraints are transformed into equality constraints by the addition of non-negative slack variables. To solve the modified problem, the non-negativity condition of the slack variables is treated by a modified barrier function with quadratic extrapolation. The modified problem is transformed into a Lagrangian problem, whose solution is determined by the application of the first-order necessary optimality conditions. In the discrete penalty- modified barrier Lagrangian function algorithm, a sequence of modified problems is successively solved until all the variables of the modified problem that are associated with the discrete variables of the original problem assume discrete values. The efectiveness of the proposed model and the robustness of this approach for solving reactive optimal power flow problems were verified with the IEEE 14, 30, 57 and 118-bus test systems and the 440 kV CESP 53-bus equivalent system. The results show that the proposed approach for solving nonlinear programming problems successfully handles discrete variables and complementarity constraints.
Barrera, Estevez Michael [Verfasser], Andreas Akademischer Betreuer] [Gutachter] Reichert und Amparo [Gutachter] [Acker-Palmer. „Functional role of OPA1 in mitochondrial membrane structure and quality control / Michael Barrera Estevez. Betreuer: Andreas Reichert. Gutachter: Amparo Acker-Palmer ; Andreas Reichert“. Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2016. http://d-nb.info/1112601430/34.
Der volle Inhalt der QuelleBarrera, Estevez Michael Verfasser], Andreas [Akademischer Betreuer] [Gutachter] Reichert und Amparo [Gutachter] [Acker-Palmer. „Functional role of OPA1 in mitochondrial membrane structure and quality control / Michael Barrera Estevez. Betreuer: Andreas Reichert. Gutachter: Amparo Acker-Palmer ; Andreas Reichert“. Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2016. http://d-nb.info/1112601430/34.
Der volle Inhalt der QuelleBücher zum Thema "Control Barrier Function"
Xiao, Wei, Christos G. Cassandras und Calin Belta. Safe Autonomy with Control Barrier Functions. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27576-0.
Der volle Inhalt der Quelle1952-, Woods David D., und United States. National Aeronautics and Space Administration., Hrsg. Breaking down barriers in cooperative fault management: Temporal and functional information displays. [Washington, DC: National Aeronautics and Space Administration, 1994.
Den vollen Inhalt der Quelle findenRelaxed Barrier Function Based Model Predictive Control. Logos Verlag Berlin, 2017.
Den vollen Inhalt der Quelle findenBonnet, Marie-Pierre, und Anne Alice Chantry. Placenta and uteroplacental perfusion. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198713333.003.0003.
Der volle Inhalt der QuelleSafe Autonomy with Control Barrier Functions: Theory and Applications. Springer International Publishing AG, 2023.
Den vollen Inhalt der Quelle findenSafe Autonomy with Control Barrier Functions: Theory and Applications. Springer International Publishing AG, 2024.
Den vollen Inhalt der Quelle findenBouchard, Danielle R., Hrsg. Exercise and Physical Activity for Older Adults. Human Kinetics, 2021. http://dx.doi.org/10.5040/9781718220942.
Der volle Inhalt der QuelleBuchteile zum Thema "Control Barrier Function"
Xu, Hao, Zhongjiao Shi und Liangyu Zhao. „Control Barrier Function-Based Discrete-Time Adaptive Control for Uncertain Systems“. In Lecture Notes in Electrical Engineering, 280–90. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2216-0_27.
Der volle Inhalt der QuelleWu, Zhe, und Panagiotis D. Christofides. „Operational Safety Via Control Lyapunov-Barrier Function-Based MPC“. In Process Operational Safety and Cybersecurity, 59–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71183-2_4.
Der volle Inhalt der QuelleLiu, Shifeng, Xuemei Ren und Dongdong Zheng. „Adaptive Barrier Lyapunov Function Control for Motion-Constrained Manipulator Systems“. In Lecture Notes in Electrical Engineering, 347–56. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8650-3_35.
Der volle Inhalt der QuelleSchoer, Andrew, Helena Teixeira-Dasilva, Christian So, Makai Mann und Roberto Tron. „Control Barrier Function Toolbox: An Extensible Framework for Provable Safety“. In Lecture Notes in Computer Science, 352–58. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60698-4_21.
Der volle Inhalt der QuelleWu, Bi, Junqi Wu und Hongbin Deng. „Multi-agent Formation Optimization Obstacle Avoidance Tracking Control Based on Control Barrier Function“. In Lecture Notes in Electrical Engineering, 83–93. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3336-1_8.
Der volle Inhalt der QuelleJohnston, Daniel G. W., und Sinéad C. Corr. „Toll-Like Receptor Signalling and the Control of Intestinal Barrier Function“. In Methods in Molecular Biology, 287–300. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3335-8_18.
Der volle Inhalt der QuelleWang, Xu, Yituo Song, Haoxuan Wei, Yanfang Liu und Naiming Qi. „Safety Control of Quadrotor by Control Barrier Function Based-on Data-Driven Hamilton Dynamics Model“. In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022), 3467–78. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_320.
Der volle Inhalt der QuelleJiang, Chenhuan, Hanyu Gan, Illés Vörös, Dénes Takács und Gábor Orosz. „Safety Filter for Lane-Keeping Control“. In Lecture Notes in Mechanical Engineering, 371–77. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_53.
Der volle Inhalt der QuelleGao, Qingji, Junhu Feng, Gaowei Zhang und Wenbo Cao. „Control Barrier Function Based Model Predictive Control to Safety Obstacle-Avoidance of Autonomous Manned Mobile Robots“. In Lecture Notes in Electrical Engineering, 606–14. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2216-0_58.
Der volle Inhalt der QuelleCai, Yun-song, Jing Xu und Yu-gang Niu. „Model-Free Formation Control: Multi-input Adaptive Super-Twisting Approach Based on Barrier Function“. In Lecture Notes in Electrical Engineering, 134–43. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3328-6_12.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Control Barrier Function"
Won, Seung-Beom, und Hyo-Sung Ahn. „Vision-based Formation Control with Control Barrier Function“. In 2024 24th International Conference on Control, Automation and Systems (ICCAS), 478–82. IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773299.
Der volle Inhalt der QuelleWang, Weijia, Tao Meng, Xiaofeng Zhao, Jiakun Lei und Kun Wang. „Warming up a Backup Control Barrier Function“. In 2024 China Automation Congress (CAC), 2855–60. IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865595.
Der volle Inhalt der QuelleTang, Jian, Zhiyuan Deng, Hao Zhang, Tao Jiang, Chenyang Wang und Zhiwu Ke. „Control Barrier Function-Based Quadrotor Interception Control Using Visual Servoing“. In 2024 IEEE International Conference on Unmanned Systems (ICUS), 319–24. IEEE, 2024. https://doi.org/10.1109/icus61736.2024.10839921.
Der volle Inhalt der QuelleWang, Yulu. „Trajectory Tracking Control of Quadrotors via Robust Control Barrier Function“. In 2024 43rd Chinese Control Conference (CCC), 2239–44. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662043.
Der volle Inhalt der QuelleBlack, Mitchell, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto und Danil Prokhorov. „CBFkit: A Control Barrier Function Toolbox for Robotics Applications“. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 12428–34. IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801431.
Der volle Inhalt der QuelleXu, Lihao, Xiaogang Xiong und Yang Bai. „Dynamic Control Barrier Function Based Trajectory Planning for Mobile Manipulator“. In 2024 43rd Chinese Control Conference (CCC), 3815–20. IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662784.
Der volle Inhalt der QuelleShen, Xun, Akifumi Wachi, Wataru Hashimoto, Kazumune Hashimoto und Shigemasa Takai. „Safe Reinforcement Learning Using Model Predictive Control with Probabilistic Control Barrier Function“. In 2024 American Control Conference (ACC), 74–79. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644734.
Der volle Inhalt der QuelleDong, Zi-Yuan, Xin-Yi Yu, Lin-Lin Ou und Yong-Qi Zhang. „Data-Driven Safety-Critical Control with High-Order Iterative Control Barrier Function“. In 2024 IEEE 63rd Conference on Decision and Control (CDC), 4881–86. IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886234.
Der volle Inhalt der QuelleWang, Yapeng, und Yunhai Geng. „Barrier Lypanunov Function-based Adaptive Control of an Ankle Exoskeleton“. In 2024 9th International Conference on Automation, Control and Robotics Engineering (CACRE), 350–54. IEEE, 2024. http://dx.doi.org/10.1109/cacre62362.2024.10635073.
Der volle Inhalt der QuellePark, Younghwa, und Christoffer Sloth. „Differential-Algebraic Equation Control Barrier Function for Flexible Link Manipulator“. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 12408–13. IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801482.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Control Barrier Function"
Hovakimyan, Naira, Hunmin Kim, Wenbin Wan und Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.
Der volle Inhalt der QuelleUnzeta, Bruno Bueno, Jan de Boer, Ruben Delvaeye, Bertrand Deroisy, Marc Fontoynont, David Geisler-Moroder, Niko Gentile et al. Survey on opportunities and barriers in lighting controls. Herausgegeben von Marc Fontoynont. IEA SHC Task 61, Februar 2021. http://dx.doi.org/10.18777/ieashc-task61-2021-0002.
Der volle Inhalt der QuelleBrandl, Maria T., Shlomo Sela, Craig T. Parker und Victor Rodov. Salmonella enterica Interactions with Fresh Produce. United States Department of Agriculture, September 2010. http://dx.doi.org/10.32747/2010.7592642.bard.
Der volle Inhalt der QuelleChejanovsky, Nor, und Suzanne M. Thiem. Isolation of Baculoviruses with Expanded Spectrum of Action against Lepidopteran Pests. United States Department of Agriculture, Dezember 2002. http://dx.doi.org/10.32747/2002.7586457.bard.
Der volle Inhalt der Quellede Brauw, Alan, Daniel Gilligan, Laura Leavens, Fekadu Moges, Shalini Roy und Mulugeta Tefera. Impact Evaluation of the SHARPE Programme in Ethiopia: Academic Report. Centre for Excellence and Development Impact and Learning (CEDIL), März 2023. http://dx.doi.org/10.51744/crpp6.
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