Compositional Controller Synthesis for Vehicular Traffic Networks
Eric S. Kim, Murat Arcak, and Sanjit A. Seshia. Compositional Controller Synthesis for Vehicular Traffic Networks. In Proceedings of the 54th IEEE Conference on Decision and Control (CDC), pp. 6165–6171, December 2015.
Download
Abstract
We tackle the issue of scalability when synthesizing controllers for large signalized vehicular traffic networks with linear temporal logic specifications. Traffic networks lend themselves to a compositional synthesis approach because they are naturally decomposed into sub-networks. However, naively synthesizing controllers for individual sub-networks and interconnecting them can violate the specifications on the monolithic network. By exploiting notions of supply and demand in our system dynamics, we construct contracts between sub-networks that guarantee the soundness of the overall synthesized controller. The resulting decentralized control architecture consists of controllers that rely only on local state information
BibTeX
@inproceedings{kim-cdc15, author = {Eric S. Kim and Murat Arcak and Sanjit A. Seshia}, title = {Compositional Controller Synthesis for Vehicular Traffic Networks}, booktitle = {Proceedings of the 54th IEEE Conference on Decision and Control (CDC)}, Year = {2015}, Month = {December}, pages = "6165--6171", abstract = {We tackle the issue of scalability when synthesizing controllers for large signalized vehicular traffic networks with linear temporal logic specifications. Traffic networks lend themselves to a compositional synthesis approach because they are naturally decomposed into sub-networks. However, naively synthesizing controllers for individual sub-networks and interconnecting them can violate the specifications on the monolithic network. By exploiting notions of supply and demand in our system dynamics, we construct contracts between sub-networks that guarantee the soundness of the overall synthesized controller. The resulting decentralized control architecture consists of controllers that rely only on local state information}, }