C
Christos G. Cassandras
Researcher at Boston University
Publications - 572
Citations - 16453
Christos G. Cassandras is an academic researcher from Boston University. The author has contributed to research in topics: Optimal control & Hybrid system. The author has an hindex of 49, co-authored 536 publications receiving 14702 citations. Previous affiliations of Christos G. Cassandras include Université catholique de Louvain & Charles Stark Draper Laboratory.
Papers
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Book
Introduction to Discrete Event Systems
TL;DR: This edition includes recent research results pertaining to the diagnosis of discrete event systems, decentralized supervisory control, and interval-based timed automata and hybrid automata models.
Proceedings ArticleDOI
On maximum lifetime routing in Wireless Sensor Networks
Xu Ning,Christos G. Cassandras +1 more
TL;DR: A more realistic model for battery dynamics is presented, the optimal allocation of a total energy amount over all nodes so as to maximize network lifetime is revisited, and it is proved that this is equivalent to a shortest path problem on a weighted graph and can therefore be efficiently solved.
Book
Discrete event systems : modeling and performance analysis
TL;DR: The aim of this text is to teach the student what discrete event systems are about and how they differ from "classical systems"; describe the differences between various modelling approaches; and show how to simulate DES using commercially-available software or from first principles.
Journal ArticleDOI
A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections
TL;DR: The solution of the throughput maximization problem depends only on the hard safety constraints imposed on CAVs and its structure enables a decentralized optimal control problem formulation for energy minimization, which shows substantial dual benefits of the proposed decentralized framework.
Journal ArticleDOI
Optimal control of a class of hybrid systems
TL;DR: A modeling framework for hybrid systems intended to capture the interaction of event-driven and time-driven dynamics and several properties of optimal state trajectories are identified which significantly simplify the task of obtaining explicit optimal control policies.