Neural Network-based Control for Multi-Agent Systems from Spatio-Temporal Specifications

被引:4
作者
Alsalehi, Suhail [1 ]
Mehdipour, Noushin [1 ]
Bartocci, Ezio [2 ]
Belta, Calin [1 ]
机构
[1] Boston Univ, Div Syst Engn, Boston, MA 02215 USA
[2] TU Wien, Vienna, Austria
来源
2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) | 2021年
基金
美国国家科学基金会;
关键词
D O I
10.1109/CDC45484.2021.9682921
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We propose a framework for solving control synthesis problems for multi-agent networked systems required to satisfy spatio-temporal specifications. We use Spatio-Temporal Reach and Escape Logic (STREL) as a specification language. For this logic, we define smooth quantitative semantics, which captures the degree of satisfaction of a formula by a multi-agent team. We use the novel quantitative semantics to map control synthesis problems with STREL specifications to optimization problems and propose a combination of heuristic and gradient-based methods to solve such problems. As this method might not meet the requirements of a real-time implementation, we develop a machine learning technique that uses the results of the off-line optimizations to train a neural network that gives the control inputs at current states. We illustrate the effectiveness of the proposed framework by applying it to a model of a robotic team required to satisfy a spatial-temporal specification under communication constraints.
引用
收藏
页码:5110 / 5115
页数:6
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