Summary
This study focuses on distributed finite‐time consensus control of second‐order multiagent systems (MASs) with of both input saturation and disturbances. To achieve finite‐time consensus of the MAS with input saturation, a distributed controller is designed by considering relative position and relative velocity measurements. In particular, a continuous integral sliding mode method is designed to deal with bounded disturbances. With the proposed controller, the system state enters the sliding mode from any initial state and will be stably and reliably maintained, where the disturbance can be compensated by a disturbance observer. Based on the continuous homogeneous theory, it is validated that the proposed combined protocol will guarantee finite‐time consensus of MASs in the format of second‐order integrators with input saturation and disturbances. Both the leader‐following and leaderless cases are considered and solved in this study. Finally, the effectiveness of the proposed method is verified by numerical simulations.
Collective phenomenon of natural animal groups will be attributed to individual intelligence and interagent interactions, where a long-standing challenge is to reveal the causal relationship among individuals. In this study, we propose a causal inference method based on information theory. More precisely, we calculate mutual information by using a data mining algorithm named “k-nearest neighbor” and subsequently induce the transfer entropy to obtain the causality entropy quantifying the causal dependence of one individual on another subject to a condition set consisting of other neighboring ones. Accordingly, we analyze the high-resolution GPS data of three pigeon flocks to extract the hidden interaction mechanism governing the coordinated free flight. The comparison of spatial distribution between causal neighbors and all other remainders validates that no bias exists for the causal inference. We identify the causal relationships to establish the interaction network and observe that the revealed causal relationship follows a local interaction mode. Interestingly, the individuals closer to the mass center and the average velocity direction are more influential than others.
To understand the collective behaviors of biological swarms, flocks, and colonies, we investigated the non-equilibrium dynamic patterns of self-propelled particle systems using statistical mechanics methods and H-stability analysis of Hamiltonian systems. By varying the individual vision range, we observed phase transitions between four phases, i.e., gas, crystal, liquid, and mill-liquid coexistence patterns. In addition, by varying the inter-particle force, we detected three distinct milling sub-phases, i.e., ring, annulus, and disk. Based on the coherent analysis for collective motions, one may predict the stability and adjust the morphology of the phases of self-propelled particles, which has promising potential applications in natural self-propelled particles and artificial multi-agent systems.
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