Distributed model predictive control for continuous‐time nonlinear systems based on suboptimal ADMM

This paper presents a distributed model predictive control (DMPC) scheme for continuous‐time nonlinear systems based on the alternating direction method of multipliers (ADMM). A stopping criterion in the ADMM algorithm limits the iterations and therefore the required communication effort during the DMPC solution at the expense of a suboptimal solution. Stability results are presented for the suboptimal DMPC scheme under two different ADMM convergence assumptions. In particular, it is shown that the required iterations in each ADMM step are bounded, which is also confirmed in simulation studies.

本文提出了一种基于交替方向乘子法（ADMM）的连续时间非线性系统分布式模型预测控制（DMPC）方案。ADMM算法中的一个停止准则限制了迭代次数，从而在以次优解为代价的情况下减少了DMPC求解过程中所需的通信量。在两种不同的ADMM收敛假设下，给出了次优DMPC方案的稳定性结果。特别地，证明了每个ADMM步骤中所需的迭代次数是有界的，这在仿真研究中也得到了证实。