Abstract
In this paper, a class of multi-agent flocking control algorithms are contrived, which can specify communication networking, inter-agent distance and position-pattern subgrouping for second-order particle multi-agent systems, by embedding Olfati-Saber's algorithm with what we call the topological specification matrices/vectors. In other words, by equipping Olfati-Saber's algorithms with more weighting factors, the steady-state formation behaviours in a concerned multi-agent system can be manoeuvered while collision-free flocking is induced. More specifically, we create novel collective potential functions with the topological specification matrices/vectors to evaluate multi-agent dynamics and achieve the expected steady-state features in terms of graph connectivity, distance keeping, position-pattern-related subgrouping and obstacle avoidance. Moreover, formation convergence can be improved by integral specification of position difference. Numerical simulations well demonstrate effectiveness of the proposed algorithms.
Disclosure statement
No potential conflict of interest was reported by the author(s).