Small Noise May Diversify Collective Motion in Vicsek Model

• Published in: IEEE transactions on automatic control Vol. 62; no. 2; pp. 636 - 651
• Main Author: Chen, Ge
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Biological system modeling; Boundary conditions; Collective motion; Complex systems; Control algorithms; Cooperative control; Dynamical systems; heterogeneous multi-agent system; Mathematical model; Noise; Noise intensity; Nonlinear dynamics; Phase transitions; Population density; Randomness; robust consensus; Robustness; Self organizing systems; self-propelled particles; Sociology; Statistics; Vicsek model
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2016.2560144

Natural systems are inextricably affected by noise. Within recent decades, the manner in which noise affects the collective behavior of self-organized systems, specifically, has garnered considerable interest from researchers and developers in various fields. To describe the collective motion of multiple interacting particles, Vicsek et al. proposed a well-known self-propelled particle (SPP) system, which exhibits a second-order phase transition from disordered to ordered motion in simulation; due to its non-equilibrium, randomness, and strong coupling nonlinear dynamics, however, there has been no rigorous analysis of such a system to date. To decouple systems consisting of deterministic laws and randomness, we propose a general method which transfers the analysis of these systems to the design of cooperative control algorithms. In this study, we rigorously analyzed the original Vicsek model under both open and periodic boundary conditions for the first time, and developed extensions to heterogeneous SPP systems (including leader-follower models) using the proposed method. Theoretical results show that SPP systems switch an infinite number of times between ordered and disordered states for any noise intensity and population density, which implies that the phase transition indeed takes a nontraditional form. We also investigated the robust consensus and connectivity of these systems. Moreover, the findings presented in this paper suggest that our method can be used to predict possible configurations during the evolution of complex systems, including turn, vortex, bifurcation, and flock merger phenomena as they appear in SPP systems.