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针对复杂功率驱动系统控制中现有牵制控制策略在保持拓扑结构连通性和降低功率消耗方面存在的不是,提出一种基于局部估计的功率驱动多智能体网络的牵制蜂拥控制算法.该算法首先利用幂迭代一致估计算法动态估计多智能体网络的代数连通度;然后根据代数连通度的局部估计值及最大功率约束自适应调整个体发射半径,以保持多智能体网络在演化过程中拓扑结构的连通性,并有效降低功率消耗;最后通过仿真实验验证了所提出控制算法的有效性.
In order to keep the topological structure connectivity and reduce the power consumption, the existing detention control strategy in the control of complex power-driven systems is not based on the local estimation, and a de-entangled control algorithm based on local estimation for power-driven multi-agent networks is proposed. Firstly, Then the algebraic connectivity of multi-agent networks is dynamically estimated by the power-iterative consensus estimation algorithm. Then, the emission radius of individuals is adaptively adjusted according to the local estimates of algebraic connectivity and the maximum power constraint, so as to keep the connectivity of the topology of multi-agent networks evolving And reduce the power consumption effectively. Finally, the simulation results show the effectiveness of the proposed control algorithm.