A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)
A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)
复制标题
DOI：
10.1007/s11081-021-09605-3
复制
发表时间：
2020-10
影响因子：
2.1
通讯作者：
Daniel Burk;Andreas Völz;K. Graichen
中科院分区：
文献类型：
--
作者：
Daniel Burk;Andreas Völz;K. Graichen
提出了一种分布式系统模型预测控制的模块化开源框架GRAMPC-D。模块化概念允许使用相同的问题描述以集中和分布式方式解决最优控制问题。它是专门针对计算效率，重点是嵌入式硬件。分布式求解基于交替方向乘子法，并利用邻域逼近的概念提高收敛速度。所提出的框架可以通过C++和Python访问，并且还支持即插即用和通过网络在代理之间进行数据交换。
The modular open-source framework GRAMPC-D for model predictive control of distributed systems is presented in this paper. The modular concept allows to solve optimal control problems in a centralized and distributed fashion using the same problem description. It is tailored to computational efficiency with the focus on embedded hardware. The distributed solution is based on the alternating direction method of multipliers and uses the concept of neighbor approximation to enhance convergence speed. The presented framework can be accessed through C++ and Python and also supports plug-and-play and data exchange between agents over a network.