Flocking of Second-Order Multiagent Systems With Connectivity Preservation Based on Algebraic Connectivity Estimation

Abstract

The problem of flocking of second-order multiagent systems with connectivity preservation is investigated in this paper. First, for estimating the algebraic connectivity as well as the corresponding eigenvector, a new decentralized inverse power iteration scheme is formulated. Then, based on the estimation of the algebraic connectivity, a set of distributed gradient-based flocking control protocols is built with a new class of generalized hybrid potential fields which could guarantee collision avoidance, desired distance stabilization, and the connectivity of the underlying communication network simultaneously. What is important is that the proposed control scheme allows the existing edges to be broken without violation of connectivity constraints, and thus yields more flexibility of motions and reduces the communication cost for the multiagent system. In the end, nontrivial comparative simulations and experimental results are performed to demonstrate the effectiveness of the theoretical results and highlight the advantages of the proposed estimation scheme and control algorithm.

Distributed flocking for disturbed multiple unicycle systems under directed topologies

“…From the definitions of π i and π i j , we can see that less communications are adopted, which has reduced the energy consumption. Fang et al and Yoo and Park) have considered the relaxation on the constraints for connectivity preservation, the main drawback of which collision is avoided among all agents. In order to improve the control algorithm, some new potential functions ψ i and ψ i j are introduced to avoid collisions among all agents.…”

Design of Connectivity Preserving Flocking Using Control Lyapunov Function

“…Regarding this issue, a connectivity preserving mechanism is required to maintain connectivity during the flock movement. Fang et al in [10] further divide connectivity preservation into two main categories, that is, conservative connectivity preservation and flexible connectivity preservation. In conservative connectivity preservation, the approach is to preserve all existing links; thus the network topology is strictly fixed, as presented in [7,11,12].…”

Adaptive neural control for multiagent systems with asymmetric time‐varying state constraints and input saturation

“…The distributed cooperative control problem of nonlinear multiagent systems (MASs) has attracted substantial attention in the past decades, and many outstanding results have been developed. [1][2][3][4][5][6][7][8] The related extensive application fields include mobile robots, 9,10 unmanned air vehicle formations, 11,12 and sensor networks. 13,14 One of the basic research directions of distributed cooperative control is consensus, which means that the agents can reach an agreement with respect to a certain number of interested variables by interacting with their local neighbors.…”