Wang Qishao, Duan Zhisheng, Lv Yuezu, Wang Qingyun, Chen Guanrong
IEEE Trans Cybern. 2021 Jun;51(6):2905-2915. doi: 10.1109/TCYB.2020.3001347. Epub 2021 May 18.
The optimal consensus problem of asynchronous sampling single-integrator and double-integrator multiagent systems is solved by distributed model predictive control (MPC) algorithms proposed in this article. In each predictive horizon, the finite-time linear-quadratic performance is minimized distributively by the control input with consensus state optimization. The MPC technique is then utilized to extend the optimal control sequence to the case of an infinite horizon. Conditions depending only on each agent's weighting scalar and sampling step are derived to guarantee the stability of the closed-loop system. Numerical examples of rendezvous control of multirobot systems illustrate the efficiency of the proposed algorithm.
本文提出的分布式模型预测控制（MPC）算法解决了异步采样单积分器和双积分器多智能体系统的最优一致性问题。在每个预测时域内，通过具有一致性状态优化的控制输入分布式地最小化有限时间线性二次性能。然后利用MPC技术将最优控制序列扩展到无限时域的情况。推导了仅依赖于每个智能体的加权标量和采样步长的条件，以保证闭环系统的稳定性。多机器人系统交会控制的数值例子说明了所提算法的有效性。
