School of Automation, Key Laboratory of Image Processing and Intelligent Control, State Key Laboratory of Digital Manufacturing Equipments and Technology, Huazhong University of Science and Technology, Wuhan 430074, PR China.
School of Automation, Key Laboratory of Image Processing and Intelligent Control, State Key Laboratory of Digital Manufacturing Equipments and Technology, Huazhong University of Science and Technology, Wuhan 430074, PR China.
ISA Trans. 2015 Sep;58:112-20. doi: 10.1016/j.isatra.2015.03.011. Epub 2015 Apr 14.
This paper addresses model predictive control schemes for consensus in multi-agent systems (MASs) with discrete-time single-integrator dynamics under switching directed interaction graphs. The control horizon is extended to be greater than one which endows the closed-loop system with extra degree of freedom. We derive sufficient conditions on the sampling period and the interaction graph to achieve consensus by using the property of infinite products of stochastic matrices. Consensus can be achieved asymptotically if the sampling period is selected such that the interaction graph among agents has a directed spanning tree jointly. Significantly, if the interaction graph always has a spanning tree, one can select an arbitrary large sampling period to guarantee consensus. Finally, several simulations are conducted to illustrate the effectiveness of the theoretical results.
本文针对离散时间单积分动力学下具有切换有向交互图的多智能体系统（MASs）的一致性问题，提出了模型预测控制方案。控制时域扩展到大于 1，这为闭环系统提供了额外的自由度。我们利用随机矩阵无穷乘积的性质，推导出了在采样周期和交互图上达成一致的充分条件。如果选择的采样周期使得智能体之间的交互图具有共同的有向生成树，那么可以渐近地实现一致性。显著地，如果交互图总是有生成树，则可以选择任意大的采样周期来保证一致性。最后，进行了几个仿真来验证理论结果的有效性。
