Nonconvex distributed feedback optimization for aggregative cooperative robotics
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Decentralized and swarm-based systems manage larger scales with moderate to high overhead.
“Table 7 compares these architectures, showing decentralized and swarm-based systems managing larger scales with moderate to high overhead [44,51].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [51]
The Introduction (page not available) describes a fully distributed method for a network of agents, and the Problem Formulation section specifies continuous inter-agent communication. But the paper’s simulation uses six agents; our copy does not establish that decentralized or swarm-based systems manage larger scales, or classify their overhead as moderate to high. (LLM, conf=0.83)
Experimental — accuracy not yet measuredScalability is limited by coordination overhead in large fleets, with distributed optimization reducing latency in swarms, yet efficiency drops remain a concern.
“Scalability is limited by coordination overhead in large fleets, with distributed optimization reducing latency in swarms [51], yet efficiency drops remain a concern [4].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [51]
The Abstract describes a distributed optimization law for cooperating robots, and Section V applies it to a six-robot surveillance network. Our copy does not report reduced latency in swarms or establish that coordination overhead limits scalability in large fleets. The efficiency concern is attached to a different citation and is not assessed here. (LLM, conf=0.82)
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Cites
Sastry, S. (1999) · Interdisciplinary applied mathematics · doi:10.1007/978-1-4757-3108-8 · Cited by 2,135 · see citing works
Proceedings of 35th IEEE Conference on Decision and Control
Bernhardsson, B., & Nilsson, J. (1996) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.1996.574247 · Cited by 1,396 · see citing works
Stability of extremum seeking feedback for general nonlinear dynamic systems
Krstić, M., & Wang, H.-H. (2000) · Automatica · doi:10.1016/s0005-1098(99)00183-1 · Cited by 1,394 · see citing works
2013 American Control Conference
(2013) · doi:10.1109/acc31500.2013 · Cited by 995 · see citing works
Real‐Time Optimization by Extremum‐Seeking Control
Ariyur, K. B., & Krstić, M. (2003) · Wiley eBooks · doi:10.1002/0471669784 · Cited by 993 · see citing works
A survey of distributed optimization
Yang, T., Yi, X., Wu, J., Yuan, Y., Wu, D. X., Meng, Z., Hong, Y., Wang, H., Lin, Z., & Johansson, K. H. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.05.006 · Cited by 914 · see citing works
Low, S. H., Paganini, F., & Doyle, J. C. (2002) · IEEE Control Systems · doi:10.1109/37.980245 · Cited by 798 · see citing works
Kia, S. S., Cortés, J., & Martı́nez, S. (2015) · Automatica · doi:10.1016/j.automatica.2015.03.001 · Cited by 687 · see citing works
On non-local stability properties of extremum seeking control
Tan, Y., Nešić, D., & Mareels, I. (2006) · Automatica · doi:10.1016/j.automatica.2006.01.014 · Cited by 626 · see citing works
Distributed Algorithms for Aggregative Games on Graphs
Koshal, J., Nedić, A., & Shanbhag, U. V. (2016) · Operations Research · doi:10.1287/opre.2016.1501 · Cited by 335 · see citing works
Dall’Anese, E., & Simonetto, A. (2016) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2016.7525172 · Cited by 322 · see citing works
Distributed Optimization for Control
Nedić, A., & Liu, J. Y. (2018) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-060117-105131 · Cited by 302 · see citing works
Tang, Y., Dvijotham, K., & Low, S. H. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2704922 · Cited by 253 · see citing works
Teel, A. R., & Popović, D. B. (2001) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2001.946111 · Cited by 180 · see citing works
Online Optimization as a Feedback Controller: Stability and Tracking
Colombino, M., Dall’Anese, E., & Bernstein, A. (2019) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2019.2906916 · Cited by 149 · see citing works
Distributed Generalized Nash Equilibrium Seeking in Aggregative Games on Time-Varying Networks
Belgioioso, G., Nedić, A., & Grammatico, S. (2020) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.3005922 · Cited by 148 · see citing works
On the stable equilibrium points of gradient systems
Absil, P.-A., & Kurdyka, K. (2006) · Systems & Control Letters · doi:10.1016/j.sysconle.2006.01.002 · Cited by 140 · see citing works
Distributed Aggregative Optimization Over Multi-Agent Networks
Li, X., Xie, L., & Hong, Y. (2021) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2021.3095456 · Cited by 112 · see citing works
Timescale Separation in Autonomous Optimization
Hauswirth, A., Bolognani, S., Hug, G., & Dörfler, F. (2020) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.2989274 · Cited by 105 · see citing works
Distributed Optimization for Smart Cyber-Physical Networks
Notarstefano, G., Notarnicola, I., & Camisa, A. (2019) · Foundations and Trends® in Systems and Control · doi:10.1561/2600000020 · Cited by 99 · see citing works
Self-citationOn Constrained Steady-State Regulation: Dynamic KKT Controllers
Jokić, A., Lazar, M., & van den Bosch, P. P. J. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2009.2026856 · Cited by 96 · see citing works
Distributed Online Convex Optimization With an Aggregative Variable
Li, X., Yi, X., & Xie, L. (2021) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2021.3107480 · Cited by 95 · see citing works
Gadjov, D., & Pavel, L. (2020) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.3015354 · Cited by 91 · see citing works
Hamiltonian and Gradient Flows, Algorithms and Control
Bloch, A. M. (1995) · American Mathematical Society eBooks · doi:10.1090/fic/003 · Cited by 89 · see citing works
Distributed Online Aggregative Optimization for Dynamic Multirobot Coordination
Carnevale, G., Camisa, A., & Notarstefano, G. (2022) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2022.3196627 · Cited by 73 · see citing works
Self-citationTime-Varying Optimization of LTI Systems Via Projected Primal-Dual Gradient Flows
Bianchin, G., Cortés, J., Poveda, J. I., & Dall’Anese, E. (2021) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2021.3112762 · Cited by 67 · see citing works
Stability of Dynamic Feedback optimization with Applications to Power Systems
Menta, S., Hauswirth, A., Bolognani, S., Hug, G., & Dörfler, F. (2018) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2018.8635640 · Cited by 50 · see citing works
Non-Convex Feedback Optimization with Input and Output Constraints
Häberle, V., Hauswirth, A., Ortmann, L., Bolognani, S., & Dörfler, F. (2020) · IEEE Control Systems Letters · doi:10.1109/lcsys.2020.3002152 · Cited by 49 · see citing works
Feedback design for multi-agent systems: A saddle point approach
Brunner, F. D., Dürr, H.-B., & Ebenbauer, C. (2012) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2012.6426476 · Cited by 47 · see citing works
Recent results on Lyapunov-theoretic techniques for nonlinear stability
Lin, Y., Sontag, E. D., & Wang, Y. (2005) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.1994.752377 · Cited by 41 · see citing works
Model-Free Nonlinear Feedback Optimization
He, Z., Bolognani, S., He, J., Dörfler, F., & Guan, X. (2023) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2023.3341752 · Cited by 37 · see citing works
Aggregative feedback optimization for distributed cooperative robotics
Carnevale, G., Mimmo, N., & Notarstefano, G. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.07.227 · Cited by 35 · see citing works
Self-citationOptimal Steady-State Control for Linear Time-Invariant Systems
Lawrence, L., Nelson, Z. E., Mallada, E., & Simpson-Porco, J. W. (2018) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2018.8619812 · Cited by 34 · see citing works
Tracking-Based Distributed Equilibrium Seeking for Aggregative Games
Carnevale, G., Fabiani, F., Fele, F., Margellos, K., & Notarstefano, G. (2024) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2024.3368967 · Cited by 32 · see citing works
Self-citationDistributed projection‐free algorithm for constrained aggregative optimization
Wang, T., & Yi, P. (2023) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.6640 · Cited by 31 · see citing works
Triggered Gradient Tracking for asynchronous distributed optimization
Carnevale, G., Notarnicola, I., Marconi, L., & Notarstefano, G. (2022) · Automatica · doi:10.1016/j.automatica.2022.110726 · Cited by 28 · see citing works
Self-citationAsynchronous and Time-Varying Proximal Type Dynamics in Multiagent Network Games
Cenedese, C., Belgioioso, G., Kawano, Y., Grammatico, S., & Cao, M. (2020) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.3011916 · Cited by 28 · see citing works
48th Annual Allerton Conference On Communication, Control, And Computing
(2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2010.2050532 · Cited by 25 · see citing works
Distributed aggregative optimization with quantized communication
Chen, Z., & Liang, S. (2022) · Kybernetika · doi:10.14736/kyb-2022-1-0123 · Cited by 19 · see citing works
Anti-Windup Approximations of Oblique Projected Dynamics for Feedback-based OptimizationPreprint
Hauswirth, A., Dörfler, F., & Teel, A. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00478 · Cited by 13 · see citing works
A Learning-based Distributed Algorithm for Personalized Aggregative Optimization
Carnevale, G., & Notarstefano, G. (2022) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc51059.2022.9992678 · Cited by 7 · see citing works
Self-citationDistributed Feedback Optimisation for Robotic Coordination
Terpin, A., Fricker, S., Perez, M., de Badyn, M. H., & Dörfler, F. (2022) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc53348.2022.9867738 · Cited by 7 · see citing works
Cothren, L., Bianchin, G., & Dall’Anese, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.01652 · Cited by 6 · see citing works
Data-based Online Optimization of Networked Systems with Infrequent Feedback.Preprint
Ospina, A. M., Bastianello, N., & Dall’Anese, E. (2021) · arXiv (Cornell University) · Cited by 1 · see citing works
Flagged first, then by citations
2 of the 47 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- P. D. Grontas, M. W. Fisher, and F. Dörfler, "Distributed and constrained h 2 control design via system level synthesis and dual consensus admm," in 2022 IEEE 61st Conference on Decision and Control (CDC), pp. 301-307, IEEE, 2022.
- S. Sastry, Nonlinear systems: analysis, stability, and control, vol. 10. Springer Science & Business Media, 2013.
Cited by
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Testa, A., Carnevale, G., & Notarstefano, G. (2025) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3557698 · Cited by 23 · see citing works
Self-citationHybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability
Urrea, C. (2025) · Actuators · doi:10.3390/act14040177 · Cited by 19 · see citing works
Distributed Convex Optimization Over Nonlinear Networks Under Set Constraints
Zhang, J., Liu, S., & Xie, L. (2025) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2025.3535578 · Cited by 10 · see citing works
Distributed aggregative optimization with affine coupling constraints
Du, K., & Meng, M. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.107085 · Cited by 9 · see citing works
Decentralized Feedback Optimization via Sensitivity Decoupling: Stability and Sub-optimality
Wang, W., He, Z., Belgioioso, G., Bolognani, S., & Dörfler, F. (2024) · European Control Conference (ECC) · doi:10.23919/ecc64448.2024.10591093 · Cited by 5 · see citing works
Huang, J., Yang, C., & Liu, Q. (2025) · IEEE Control Systems Letters · doi:10.1109/lcsys.2025.3589177 · Cited by 4 · see citing works
Fang, X., Wen, G., & Chen, G. (2025) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2025.3617519 · Cited by 4 · see citing works
Distributed Feedback Optimization for Multi-Robot Target Encirclement and Patrolling
Pichierri, L., Carnevale, G., & Notarstefano, G. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711694 · Cited by 4 · see citing works
Self-citationLi, C., Wang, Q., & Psillakis, H. E. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3608353 · Cited by 3 · see citing works
Li, C., & Wang, Q. (2026) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2026.108403 · Cited by 2 · see citing works
Timescale Separation for Discrete-Time Nonlinear Stochastic Systems
Carnevale, G., & Notarstefano, G. (2024) · IEEE Control Systems Letters · doi:10.1109/lcsys.2024.3451792 · Cited by 2 · see citing works
Self-citationA Distributed Algorithm for Coordination in Energy Communities *
Brumali, R., Carnevale, G., Carli, R., & Notarstefano, G. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711787 · Cited by 1 · see citing works
Self-citationLi, C., & Wang, Q. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130000 · Cited by 1 · see citing works
Variance-Reduced Gradient Estimator for Nonconvex Zeroth-Order Distributed Optimization
Mu, H., Tang, Y., & Li, Z. (2025) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc63710.2025.11107900 · Cited by 1 · see citing works
Ensuring Truthfulness in Distributed Aggregative Optimization
Chen, Z., Egerstedt, M., & Wang, Y. (2025) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2025.3606265 · Cited by 1 · see citing works
Research on Trends and Key Issues in Industrial Collaborative Robots and Worker Interaction
Choi, J., Kim, N., Kim, W., & Yoon, S. (2023) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2023.21.8.61 · Cited by 1 · see citing works
Distributed ℓ0 Sparse Aggregative Optimization
Olama, A., Carnevale, G., Notarstefano, G., & Camponogara, E. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711465
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Huang, S., & Grammatico, S. (2025) · European Control Conference (ECC) · doi:10.23919/ecc65951.2025.11187029
Distributed Aggregative Optimization of High-Order Heterogeneous Integrator Systems
Xie, R., Xian, C., & Zhao, Y. (2025) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac66630.2025.11149839
Event-Triggered Distributed Aggregative Optimization of Heterogeneous High-Order Systems
Xie, R., & Zhao, Y. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-8435-2_34
A Separation Principle for Stochastic Feedback Optimization
Tang, M., Abdelgalil, M., & Poveda, J. I. (2026) · IEEE Control Systems Letters · doi:10.1109/lcsys.2026.3704531
Distributed continuous-time optimization over second-order nonlinear multi-agent systems
Li, R., & Liu, S. (2026) · International Conference on Electronic Engineering and Informatics (EEI) · doi:10.1109/eei70303.2026.11640487
Distributed Optimal Aggregative Coordination in Open Multiagent Systems
Huang, J., Liu, Q., Ning, B., & Han, Q.-L. (2026) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2026.3724511
Zhang, J., Lin, R., Zhang, H., Gao, J., Hou, S., & Liu, S. (2026) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/cis-ram71614.2026.11668480
- Authors
- Carnevale, G., Mimmo, N., & Notarstefano, G.
- Affiliations
- University of Bologna (IT)
- Corresponding author
- Carnevale, Guido
- Venue
- Automatica
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 167
- Pages
- 111767
- ISSN
- 0005-1098 (ISSN-L)
- OpenAlex
- W4399480452
- Cited by
- 23
- References
- 47
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 6 · 2023: 1
- FWCI
- 3.67
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Automatica (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (cc-by)
- Funders
- Governo Italiano Ministero degli Affari Esteri e della Cooperazione Internazionale (BR22GR01)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Extremum Seeking Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Keywords
- non-convex optimization, cooperative robotics, multi-robot systems
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