Instant Distributed Model Predictive Control for Constrained Linear Systems
Instant Distributed Model Predictive Control for Constrained Linear Systems
复制标题
约束线性系统的即时分布式模型预测控制
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
M. Inoue
中科院分区：
文献类型：
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作者：
Martin Figura;L. Su;V. Gupta;M. Inoue
分布式最佳控制已成为一种令人兴奋的可能性。但是，现有算法倾向于需要过度的计算时间，因此可能无法稳定具有快速动力学的系统。我们开发了即时分布式模型预测控制（IDMPC），并通过实现控制器动力学中嵌入的原始二算法的实现。在快速通信的假设下，我们表明IDMPC的输入和状态轨迹等同于集中式的次优MPC方案。我们利用耗散性分析表明，闭环系统轨迹渐近地收敛于所需的参考。
Distributed optimal control has emerged as an exciting possibility; however, existing algorithms tend to require excessive computational time and thus may not be able to stabilize systems with fast dynamics. We develop instant distributed model predictive control (iDMPC) with a realization of the primal-dual algorithm embedded in the controller dynamics. Under assumptions on fast communication, we show that the input and state trajectories of iDMPC are equivalent to a centralized suboptimal MPC scheme. We utilize a dissipativity analysis to show that the closed-loop system trajectories asymptotically converge to a desired reference.