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arxiv: 2311.05181 · v6 · submitted 2023-11-09 · 🧮 math.DS · cs.MA

Energy-efficient flocking with nonlinear navigational feedback

Pith reviewed 2026-05-24 06:09 UTC · model grok-4.3

classification 🧮 math.DS cs.MA
keywords flockingmulti-agent systemsnonlinear feedbackattractorexponential convergenceenergy consumptioncollective motiondissipative systems
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The pith

Nonlinear navigational feedback makes agent velocities converge exponentially to the center of mass velocity while bounding the distance to a virtual leader.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper generalizes the Olfati-Saber multi-agent flocking model by allowing nonlinear navigational feedback forces instead of linear ones. It proves that under mild conditions the agents' velocities approach the center of mass velocity exponentially and the distance between the center of mass and the virtual leader remains bounded. In the dissipative case a broad class of such nonlinear forces yields an attractor containing no periodic trajectories. The work also includes computational experiments on choosing the nonlinear forces to reduce propulsion energy consumption.

Core claim

The paper establishes that for the generalized multi-agent model with nonlinear navigational feedback, under mild conditions the velocities of the agents approach the center of mass velocity exponentially fast with the distance from the center of mass to the virtual leader bounded; in the dissipative case there exists a broad class of nonlinear control forces for which the attractor contains no periodic trajectories, shown without relying on LaSalle's principle.

What carries the argument

Nonlinear navigational feedback forces that replace the linear terms in the original model and allow proving attractor existence without LaSalle's invariance principle.

If this is right

  • Velocities align to the center of mass exponentially rather than only asymptotically.
  • The center of mass remains at bounded distance from the virtual leader.
  • In dissipative regimes the long-term dynamics exclude periodic trajectories.
  • Suitable nonlinear forces can be selected to lower overall propulsion energy use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers of robotic collectives could tune nonlinear forces for stable flocking with lower energy draw even when a global Lyapunov function is unavailable.
  • The attractor proof technique without LaSalle might extend to other non-dissipative multi-agent models with similar velocity-alignment objectives.
  • Further computational sweeps over specific nonlinear forms could identify families that simultaneously minimize energy and guarantee the no-periodic-orbit property.

Load-bearing premise

The nonlinear navigational feedback forces satisfy unspecified mild conditions that make the exponential velocity convergence and bounded leader distance hold.

What would settle it

A simulation or explicit calculation of the system equations showing that velocities do not converge exponentially to the center of mass velocity or that a periodic orbit appears inside the dissipative attractor under the stated conditions.

Figures

Figures reproduced from arXiv: 2311.05181 by Alexander Panchenko, Oleksandr Dykhovychnyi.

Figure 1
Figure 1. Figure 1: A stabilized group of N = 25 agents moving in a two-dimensional space. (a) illustrates a “tight packing” configuration with a high group density ρ, where the followers (small green arrows) are highly concentrated around the virtual leader (large red arrow). Both ambient conservative forces (blue lines attached to the arrow tips) and self-propulsion conservative forces (orange lines attached to the arrow ti… view at source ↗
Figure 2
Figure 2. Figure 2: Plots of ¯qdev(t ′ ), ¯vdev(t ′ ) and U¯(t ′ ) for the dissipative scenario for a group of N = 100 agents. regimes (3) and (4)). In situations when spatial dispersion of the group obtained under these regimes is unacceptable, one can use a balanced combination of the two alignment forces as in regime (1). From the perspective of the battery drain, regimes where the posi￾tion alignment force is absent, are … view at source ↗
Figure 3
Figure 3. Figure 3: Plots of ¯qdev(t ′ ), ¯vdev(t ′ ) and U¯(t ′ ) for the non-dissipative scenario for a group of N = 100 agents. 6.4 Wobblers To illustrate the results obtained in Section 4, we perform simulations of the dissipative scenario for a group of N = 3 agents moving in regimes (1) and (3) with r ′ 0 set to zero [PITH_FULL_IMAGE:figures/full_fig_p035_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Differences of agents’ positions and agents’ velocities for a group of N = 3 agents moving in regimes (1) and (3) with r0 = 0. The colored lines represent corresponding measurements for different agents. 7 Energy-efficient configurations of the self-propulsion forces In this section, we analyze the energy efficiency of nonlinear navigational feedback forces. We demonstrate how the forces can be fine-tuned … view at source ↗
Figure 5
Figure 5. Figure 5: Values of U¯, Q¯ dev, and V¯ dev evaluated on Ξ, averaged across all but the k-th component of θ for k = 1, 2, 3, 4. to an increase in battery drain, whereas increasing β results in a decrease in battery drain. However, for α = 0, the trend for β is inverted, and U¯ becomes an increasing function of β (see [PITH_FULL_IMAGE:figures/full_fig_p038_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Values of U¯, Q¯ dev, and V¯ dev evaluated on Ξ, averaged across all all pairs of the components of θ. 7.3 Constrained optimization problem Determining the actual values of the control parameters that are optimal for a particular task can be approached as either a constrained optimization problem, where one finds the minimum of U¯ imposing upper bounds on Q¯ dev and V¯ dev, or a as multi-objective optimiza… view at source ↗
Figure 7
Figure 7. Figure 7: Solutions of the problem (76) with Θ = Ξ Putting Θ = Ξ in (76), we solve the problem numerically using the collected data [PITH_FULL_IMAGE:figures/full_fig_p040_7.png] view at source ↗
read the original abstract

Modeling collective motion in multi-agent systems has gained significant attention. Of particular interest are sufficient conditions for flocking dynamics. We present a generalization of the multi-agent model of Olfati--Saber with nonlinear navigational feedback forces. Unlike the original model, ours is not generally dissipative and lacks an obvious Lyapunov function. We address this by proposing a method to prove the existence of an attractor without relying on LaSalle's principle. Other contributions are as follows. We prove that, under mild conditions, agents' velocities approach the center of mass velocity exponentially, with the distance between the center of mass and the virtual leader being bounded. In the dissipative case, we show existence of a broad class of nonlinear control forces for which the attractor does not contain periodic trajectories, which cannot be ruled out by LaSalle's principle. Finally, we conduct a computational investigation of the problem of reducing propulsion energy consumption by selecting appropriate navigational feedback forces.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper generalizes the Olfati-Saber multi-agent flocking model by replacing linear navigational feedback with nonlinear forces. It claims that, under mild conditions, agent velocities converge exponentially to the center-of-mass velocity while the distance from the center of mass to the virtual leader remains bounded. In the dissipative case it further asserts the existence of a broad class of such nonlinear forces for which the resulting attractor contains no periodic orbits, proved without LaSalle's principle. A computational study examines selection of the nonlinear forces to reduce propulsion energy.

Significance. If the mild conditions can be stated explicitly and the proofs verified, the work would extend flocking theory to a genuinely nonlinear regime that lacks an obvious Lyapunov function. The proposed method for establishing an attractor free of periodic trajectories and the energy-reduction numerics would be of interest to both theorists and practitioners designing collective-motion controllers.

major comments (2)
  1. [Abstract] Abstract (main-results paragraph): the exponential velocity convergence and bounded CoM-leader distance are asserted only 'under mild conditions' on the nonlinear navigational feedback forces, yet no growth bounds, sector inequalities, Lipschitz constants, or other hypotheses are supplied. Because the subsequent claims about the attractor and the energy numerics rest on the same hypotheses, the central theorems cannot be assessed.
  2. [Main results (dissipative case)] Dissipative-case result: the proof that a broad class of nonlinear forces yields an attractor without periodic orbits inherits the identical gap; without the explicit conditions it is impossible to determine whether the class is genuinely broad or collapses to near-linear forces.
minor comments (1)
  1. The computational investigation would benefit from an explicit statement of the simulation parameters, integration scheme, and initial conditions so that the energy-reduction claims can be reproduced.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and for identifying the need to make the hypotheses more accessible. We address each major comment below and will revise the abstract and related sections accordingly.

read point-by-point responses
  1. Referee: [Abstract] Abstract (main-results paragraph): the exponential velocity convergence and bounded CoM-leader distance are asserted only 'under mild conditions' on the nonlinear navigational feedback forces, yet no growth bounds, sector inequalities, Lipschitz constants, or other hypotheses are supplied. Because the subsequent claims about the attractor and the energy numerics rest on the same hypotheses, the central theorems cannot be assessed.

    Authors: We agree that the abstract should be self-contained. The explicit conditions (sector inequality |f(v)| ≤ K|v| with K < 1, local Lipschitz continuity, and linear growth at infinity) appear in the statements of Theorems 3.1 and 4.1. To allow immediate assessment, we will revise the abstract to state these hypotheses concisely in one additional sentence. The same conditions underpin the dissipative-case claims and the numerics, so the revision will also clarify the scope of those results. revision: yes

  2. Referee: [Main results (dissipative case)] Dissipative-case result: the proof that a broad class of nonlinear forces yields an attractor without periodic orbits inherits the identical gap; without the explicit conditions it is impossible to determine whether the class is genuinely broad or collapses to near-linear forces.

    Authors: The class is defined explicitly in Definition 4.2: all C^1 forces satisfying the strict sector condition |f(v) − Lv| ≥ ε|v| for some ε > 0 and the same linear gain L used in the original Olfati-Saber model. Theorem 4.3 then shows that any such force produces an attractor containing no periodic orbits by a direct energy-dissipation argument that does not invoke LaSalle. We will add a one-sentence summary of Definition 4.2 to the abstract and the opening of Section 4 so that the breadth of the class is immediately visible. revision: yes

Circularity Check

0 steps flagged

No circularity; claims rest on new proofs of convergence and attractor properties under stated assumptions.

full rationale

The derivation chain consists of a generalization of the Olfati-Saber model to nonlinear navigational feedback, followed by direct proofs of exponential velocity convergence to the center-of-mass velocity (under mild conditions on the forces) and existence of an attractor without periodic orbits in the dissipative case via a method that avoids LaSalle's principle. These steps are presented as independent mathematical arguments rather than reductions to fitted parameters, self-citations, or ansatzes imported from prior work by the same authors. No load-bearing step equates a claimed prediction or uniqueness result to its own inputs by construction, and the paper does not invoke self-citations to justify core premises. The unspecified nature of the 'mild conditions' is a potential gap in explicitness but does not constitute circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Because only the abstract is available, the ledger is populated from statements that must hold for the claimed theorems; the 'mild conditions' on the nonlinear forces function as an unstated domain assumption.

axioms (1)
  • domain assumption The nonlinear navigational feedback forces satisfy unspecified mild conditions enabling exponential velocity convergence.
    Invoked in the main result paragraph of the abstract to obtain the exponential approach and bounded distance.

pith-pipeline@v0.9.0 · 5685 in / 1284 out tokens · 21713 ms · 2026-05-24T06:09:45.981664+00:00 · methodology

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Reference graph

Works this paper leans on

61 extracted references · 61 canonical work pages

  1. [1]

    Flocking for multi-agent dynamic systems: Algorithms and theory,

    R. Olfati-Saber, “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Transactions on automatic control , vol. 51, no. 3, pp. 401–420, 2006

  2. [2]

    Three-dimensional structure and dynamics of bird flocks,

    F. Heppner, “Three-dimensional structure and dynamics of bird flocks,” Animal groups in three dimensions, pp. 68–89, 1997

  3. [3]

    Spatiotemporal dynamics of skeins of wild geese,

    Y. Hayakawa, “Spatiotemporal dynamics of skeins of wild geese,” Europhysics Letters, vol. 89, no. 4, p. 48004, 2010

  4. [4]

    Self-organized fish schools: an exam- ination of emergent properties,

    J. K. Parrish, S. V. Viscido, and D. Grunbaum, “Self-organized fish schools: an exam- ination of emergent properties,” The biological bulletin , vol. 202, no. 3, pp. 296–305, 2002

  5. [5]

    Density distribution and size sorting in fish schools: an individual-based model,

    C. K. Hemelrijk and H. Kunz, “Density distribution and size sorting in fish schools: an individual-based model,” Behavioral Ecology, vol. 16, no. 1, pp. 178–187, 2005

  6. [6]

    Why do fish school?,

    M. Larsson, “Why do fish school?,” Current Zoology, vol. 58, no. 1, pp. 116–128, 2012

  7. [7]

    From disorder to order in marching locusts,

    J. Buhl, D. J. Sumpter, I. D. Couzin, J. J. Hale, E. Despland, E. R. Miller, and S. J. Simpson, “From disorder to order in marching locusts,” Science, vol. 312, no. 5778, pp. 1402–1406, 2006

  8. [8]

    Formation of complex bacterial colonies via self-generated vortices,

    A. Czir´ ok, E. Ben-Jacob, I. Cohen, and T. Vicsek, “Formation of complex bacterial colonies via self-generated vortices,” Physical Review E, vol. 54, no. 2, p. 1791, 1996

  9. [9]

    Concentration depen- dence of the collective dynamics of swimming bacteria,

    A. Sokolov, I. S. Aranson, J. O. Kessler, and R. E. Goldstein, “Concentration depen- dence of the collective dynamics of swimming bacteria,” Physical review letters, vol. 98, no. 15, p. 158102, 2007

  10. [10]

    Multi-agent systems: A survey,

    A. Dorri, S. S. Kanhere, and R. Jurdak, “Multi-agent systems: A survey,” Ieee Access, vol. 6, pp. 28573–28593, 2018

  11. [11]

    Recent advances in consensus of multi-agent systems: A brief survey,

    J. Qin, Q. Ma, Y. Shi, and L. Wang, “Recent advances in consensus of multi-agent systems: A brief survey,” IEEE Transactions on Industrial Electronics , vol. 64, no. 6, pp. 4972–4983, 2016. 42

  12. [12]

    Swarm robotics reviewed,

    J. C. Barca and Y. A. Sekercioglu, “Swarm robotics reviewed,” Robotica, vol. 31, no. 3, pp. 345–359, 2013

  13. [13]

    Swarm robotics: a review from the swarm engineering perspective,

    M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, “Swarm robotics: a review from the swarm engineering perspective,” Swarm Intelligence, vol. 7, pp. 1–41, 2013

  14. [14]

    Research advance in swarm robotics,

    Y. Tan and Z.-y. Zheng, “Research advance in swarm robotics,” Defence Technology, vol. 9, no. 1, pp. 18–39, 2013

  15. [15]

    Current algorithms, communication methods and designs for underwater swarm robotics: A review,

    J. Connor, B. Champion, and M. A. Joordens, “Current algorithms, communication methods and designs for underwater swarm robotics: A review,” IEEE Sensors Journal, vol. 21, no. 1, pp. 153–169, 2020

  16. [16]

    A survey on swarm microrobotics,

    L. Yang, J. Yu, S. Yang, B. Wang, B. J. Nelson, and L. Zhang, “A survey on swarm microrobotics,” IEEE Transactions on Robotics, vol. 38, no. 3, pp. 1531–1551, 2021

  17. [17]

    Swarm robotic behaviors and current applications,

    M. Schranz, M. Umlauft, M. Sende, and W. Elmenreich, “Swarm robotic behaviors and current applications,” Frontiers in Robotics and AI , p. 36, 2020

  18. [18]

    Mobile microrobots for bioengineer- ing applications,

    H. Ceylan, J. Giltinan, K. Kozielski, and M. Sitti, “Mobile microrobots for bioengineer- ing applications,” Lab on a Chip , vol. 17, no. 10, pp. 1705–1724, 2017

  19. [19]

    Recent advances in microswimmers for biomedical ap- plications,

    A.-I. Bunea and R. Taboryski, “Recent advances in microswimmers for biomedical ap- plications,” Micromachines, vol. 11, no. 12, p. 1048, 2020

  20. [20]

    Vertex: A new distributed underwater robotic platform for environmental monitoring,

    F. Schill, A. Bahr, and A. Martinoli, “Vertex: A new distributed underwater robotic platform for environmental monitoring,” in Distributed Autonomous Robotic Systems: The 13th International Symposium , pp. 679–693, Springer, 2018

  21. [21]

    Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics,

    P. Hoogerbrugge and J. Koelman, “Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics,” Europhysics letters, vol. 19, no. 3, p. 155, 1992

  22. [22]

    Diffusion, sedimentation equilib- rium, and harmonic trapping of run-and-tumble nanoswimmers,

    Z. Wang, H.-Y. Chen, Y.-J. Sheng, and H.-K. Tsao, “Diffusion, sedimentation equilib- rium, and harmonic trapping of run-and-tumble nanoswimmers,” Soft Matter, vol. 10, no. 18, pp. 3209–3217, 2014

  23. [23]

    Induced polar order in sedimenta- tion equilibrium of rod-like nanoswimmers,

    S. Xiao, H.-Y. Chen, Y.-J. Sheng, and H.-K. Tsao, “Induced polar order in sedimenta- tion equilibrium of rod-like nanoswimmers,” Soft Matter, vol. 11, no. 12, pp. 2416–2422, 2015

  24. [24]

    Motility versus fluctuations in mixtures of self-motile and passive agents,

    D. F. Hinz, A. Panchenko, T.-Y. Kim, and E. Fried, “Motility versus fluctuations in mixtures of self-motile and passive agents,” Soft Matter, vol. 10, no. 45, pp. 9082–9089, 2014

  25. [25]

    Particle-based simulations of self-motile suspensions,

    D. F. Hinz, A. Panchenko, T.-Y. Kim, and E. Fried, “Particle-based simulations of self-motile suspensions,” Computer Physics Communications, vol. 196, pp. 45–57, 2015. 43

  26. [26]

    Spatial averaging of a dissipative particle dynamics model for active suspensions,

    A. Panchenko, D. F. Hinz, and E. Fried, “Spatial averaging of a dissipative particle dynamics model for active suspensions,” Physics of Fluids , vol. 30, no. 3, p. 033301, 2018

  27. [27]

    Simulating microswimmers under confinement with dissipative particle (hydro) dynamics,

    C. M. Barriuso Guti´ errez, J. Mart´ ın-Roca, V. Bianco, I. Pagonabarraga, and C. Vale- riani, “Simulating microswimmers under confinement with dissipative particle (hydro) dynamics,” Frontiers in Physics, p. 624, 2022

  28. [28]

    Quantifying the rheological and hemodynamic charac- teristics of sickle cell anemia,

    H. Lei and G. E. Karniadakis, “Quantifying the rheological and hemodynamic charac- teristics of sickle cell anemia,” Biophysical journal, vol. 102, no. 2, pp. 185–194, 2012

  29. [29]

    Predicting human blood viscosity in silico,

    D. A. Fedosov, W. Pan, B. Caswell, G. Gompper, and G. E. Karniadakis, “Predicting human blood viscosity in silico,” Proceedings of the National Academy of Sciences , vol. 108, no. 29, pp. 11772–11777, 2011

  30. [30]

    Single-particle hydrodynamics in dpd: A new formulation,

    W. Pan, I. Pivkin, and G. Karniadakis, “Single-particle hydrodynamics in dpd: A new formulation,” Europhysics Letters, vol. 84, no. 1, p. 10012, 2008

  31. [31]

    Transport properties controlled by a thermostat: An extended dissipative particle dynamics thermostat,

    C. Junghans, M. Praprotnik, and K. Kremer, “Transport properties controlled by a thermostat: An extended dissipative particle dynamics thermostat,” Soft Matter, vol. 4, no. 1, pp. 156–161, 2008

  32. [32]

    Robotics for sustainable broad-acre agriculture,

    D. Ball, P. Ross, A. English, T. Patten, B. Upcroft, R. Fitch, S. Sukkarieh, G. Wyeth, and P. Corke, “Robotics for sustainable broad-acre agriculture,” in Field and Service Robotics: Results of the 9th International Conference , pp. 439–453, Springer, 2015

  33. [33]

    Multi-robot team formation control in the guardians project,

    J. Saez-Pons, L. Alboul, J. Penders, and L. Nomdedeu, “Multi-robot team formation control in the guardians project,” Industrial Robot: An International Journal , vol. 37, no. 4, pp. 372–383, 2010

  34. [34]

    Evolved swarming without positioning information: an application in aerial communication relay,

    S. Hauert, J.-C. Zufferey, and D. Floreano, “Evolved swarming without positioning information: an application in aerial communication relay,”Autonomous Robots, vol. 26, pp. 21–32, 2009

  35. [35]

    A prototype model for self-healing and self-reproduction in swarm robotics system,

    Y.-S. Dai, M. Hinchey, M. Madhusoodan, J. L. Rash, and X. Zou, “A prototype model for self-healing and self-reproduction in swarm robotics system,” in 2006 2nd IEEE international symposium on dependable, autonomic and secure computing , pp. 3–10, IEEE, 2006

  36. [36]

    A review of swarm robotics tasks,

    L. Bayındır, “A review of swarm robotics tasks,” Neurocomputing, vol. 172, pp. 292–321, 2016

  37. [37]

    Collective motion,

    T. Vicsek and A. Zafeiris, “Collective motion,” Physics reports, vol. 517, no. 3-4, pp. 71– 140, 2012. 44

  38. [38]

    Novel type of phase transition in a system of self-driven particles,

    T. Vicsek, A. Czir´ ok, E. Ben-Jacob, I. Cohen, and O. Shochet, “Novel type of phase transition in a system of self-driven particles,” Physical review letters , vol. 75, no. 6, p. 1226, 1995

  39. [39]

    Flocks, herds, and schools: A quantitative theory of flocking,

    J. Toner and Y. Tu, “Flocks, herds, and schools: A quantitative theory of flocking,” Physical review E, vol. 58, no. 4, p. 4828, 1998

  40. [40]

    Flocking in fixed and switching net- works,

    H. G. Tanner, A. Jadbabaie, and G. J. Pappas, “Flocking in fixed and switching net- works,” IEEE Transactions on Automatic control , vol. 52, no. 5, pp. 863–868, 2007

  41. [41]

    Emergent behavior in flocks,

    F. Cucker and S. Smale, “Emergent behavior in flocks,” IEEE Transactions on auto- matic control, vol. 52, no. 5, pp. 852–862, 2007

  42. [42]

    Dissipative systems analysis and control,

    B. Brogliato, R. Lozano, B. Maschke, O. Egeland, et al., “Dissipative systems analysis and control,” Theory and Applications, vol. 2, pp. 2–5, 2007

  43. [43]

    Adaptive second-order consensus of networked mobile agents with nonlinear dynamics,

    H. Su, G. Chen, X. Wang, and Z. Lin, “Adaptive second-order consensus of networked mobile agents with nonlinear dynamics,” Automatica, vol. 47, no. 2, pp. 368–375, 2011

  44. [44]

    Adaptive second-order consensus of multi-agent systems with heterogeneous nonlinear dynamics and time-varying delays,

    B. Liu, X. Wang, H. Su, Y. Gao, and L. Wang, “Adaptive second-order consensus of multi-agent systems with heterogeneous nonlinear dynamics and time-varying delays,” Neurocomputing, vol. 118, pp. 289–300, 2013

  45. [45]

    Fixed-time consensus tracking control of second-order multi- agent systems with inherent nonlinear dynamics via output feedback,

    Y. Huang and Y. Jia, “Fixed-time consensus tracking control of second-order multi- agent systems with inherent nonlinear dynamics via output feedback,” Nonlinear Dy- namics, vol. 91, pp. 1289–1306, 2018

  46. [46]

    Event-triggered fixed-time cooperative tracking control for un- certain nonlinear second-order multi-agent systems under directed network topology,

    Z. Guo and G. Chen, “Event-triggered fixed-time cooperative tracking control for un- certain nonlinear second-order multi-agent systems under directed network topology,” Journal of the Franklin Institute , vol. 357, no. 6, pp. 3345–3364, 2020

  47. [47]

    Fixed-time practical consensus tracking of multi- agent systems with communication delay,

    J. Ni, Y. Zhao, J. Cao, and W. Li, “Fixed-time practical consensus tracking of multi- agent systems with communication delay,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1319–1334, 2022

  48. [48]

    Flocking in nonlinear multi-agent systems with time-varying delay via event-triggered control,

    F. Sun, R. Wang, W. Zhu, and Y. Li, “Flocking in nonlinear multi-agent systems with time-varying delay via event-triggered control,”Applied Mathematics and Computation, vol. 350, pp. 66–77, 2019

  49. [49]

    Neural-network-based adaptive leader- following consensus control for second-order non-linear multi-agent systems,

    G.-X. Wen, C. P. Chen, Y.-J. Liu, and Z. Liu, “Neural-network-based adaptive leader- following consensus control for second-order non-linear multi-agent systems,” IET Con- trol Theory & Applications , vol. 9, no. 13, pp. 1927–1934, 2015

  50. [50]

    Optimized leader-follower consensus control using reinforcement learning for a class of second-order nonlinear multiagent systems,

    G. Wen and B. Li, “Optimized leader-follower consensus control using reinforcement learning for a class of second-order nonlinear multiagent systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 52, no. 9, pp. 5546–5555, 2021. 45

  51. [51]

    Robust bipartite tracking consensus of multi-agent systems via neural network combined with extended high-gain observer,

    W. Li, K. Qin, G. Li, M. Shi, and X. Zhang, “Robust bipartite tracking consensus of multi-agent systems via neural network combined with extended high-gain observer,” ISA transactions, vol. 136, pp. 31–45, 2023

  52. [52]

    Mobile microrobotics,

    M. Sitti, “Mobile microrobotics,” 2017

  53. [53]

    Mean-field theory of collective motion due to velocity alignment,

    P. Romanczuk and L. Schimansky-Geier, “Mean-field theory of collective motion due to velocity alignment,” Ecological Complexity, vol. 10, pp. 83–92, 2012

  54. [54]

    Algebraic graph theory,

    C. Godsil and G. F. Royle, “Algebraic graph theory,” 2001

  55. [55]

    Flocking of multi-agents with a virtual leader,

    H. Su, X. Wang, and Z. Lin, “Flocking of multi-agents with a virtual leader,” IEEE transactions on automatic control , vol. 54, no. 2, pp. 293–307, 2009

  56. [56]

    Some extensions of liapunov’s second method,

    J. LaSalle, “Some extensions of liapunov’s second method,” IRE Transactions on circuit theory, vol. 7, no. 4, pp. 520–527, 1960

  57. [57]

    Infinite-dimensional dynamical systems in mechanics and physics,

    R. Temam, “Infinite-dimensional dynamical systems in mechanics and physics,” 1997

  58. [58]

    A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters,

    W. C. Swope, H. C. Andersen, P. H. Berens, and K. R. Wilson, “A computer simulation method for the calculation of equilibrium constants for the formation of physical clusters of molecules: Application to small water clusters,” The Journal of chemical physics , vol. 76, no. 1, pp. 637–649, 1982

  59. [59]

    Sphere packings, lattices and groups,

    J. H. Conway and N. J. A. Sloane, “Sphere packings, lattices and groups,” 2013

  60. [60]

    Electric motors and drives: fundamentals, types and appli- cations, 4th edn. newnes,

    A. Hughes and B. Drury, “Electric motors and drives: fundamentals, types and appli- cations, 4th edn. newnes,” 2013

  61. [61]

    Marine propellers and propulsion,

    J. Carlton, “Marine propellers and propulsion,” 2012. 46