Corpus ID: 237572071

Analysis, Optimization, Control, and Learning of Cyber-Physical Systems

@inproceedings{Malikopoulos2021AnalysisOC,
  title={Analysis, Optimization, Control, and Learning of Cyber-Physical Systems},
  author={Andreas A. Malikopoulos},
  year={2021}
}
The overarching goal of the Information and Decision Science (IDS) Lab is to enhance understanding of complex systems and establish a holistic, multifaceted approach using scalable data and informatics to developing rigorous mathematical models and decentralized control algorithms for making engineering complex systems able to realize how to improve their performance over time while interacting with their environment. The emphasis is on applications related to connected and automated vehicles… Expand

References

SHOWING 1-10 OF 110 REFERENCES
On the Traffic Impacts of Optimally Controlled Connected and Automated Vehicles
TLDR
It is shown that introducing of CAVs yields radically improved roadway capacity and network performance, and a decentralized optimal control framework in a transportation network is applied. Expand
Conditions for State and Control Constraint Activation in Coordination of Connected and Automated Vehicles
TLDR
The nature of the unconstrained problem is investigated and conditions under which the state and control constraints become active are provided and a closed-form analytical solution of the constrained optimization problem is derived and evaluated using numerical simulation. Expand
Optimal Vehicle Dynamics and Powertrain Control for Connected and Automated Vehicles
TLDR
This paper presents a two-level control architecture for a connected and automated plug-in hybrid electric vehicle to optimize simultaneously its speed profile and powertrain efficiency. Expand
Cyber physical system approach for design of power grids: A survey
Cyber physical systems (CPSs) refer to the class of systems which offer close integration of computation, networking, and physical processes. CPS approach to system design has been conventionallyExpand
Centralized stochastic optimal control of complex systems
TLDR
A multiobjective optimization problem of the one-stage expected costs of the subsystems is formulated and a framework to prove that the control policy yielding the Pareto optimal solution minimizes the average cost criterion of the system is provided. Expand
Control-Theoretic Methods for Cyberphysical Security: Geometric Principles for Optimal Cross-Layer Resilient Control Systems
TLDR
The integration of cybertechnologies with physical processes increases system efficiencies and, at the same time, introduces vulnerabilities that undermine the reliability of critical infrastructures. Expand
Submodularity in Input Node Selection for Networked Linear Systems: Efficient Algorithms for Performance and Controllability
Networked systems are systems of interconnected components in which the dynamics of each component (often referred to as a node or agent) are influenced by the behavior of neighboring components.Expand
Pareto Efficient Policy for Supervisory Power Management Control
  • Andreas A. Malikopoulos
  • Computer Science, Engineering
  • 2015 IEEE 18th International Conference on Intelligent Transportation Systems
  • 2015
TLDR
This paper model HEV operation as a controlled Markov chain using the long-run expected average cost per unit time criterion, and shows that the control policy yielding the Pareto optimal solution minimizes the average cost criterion online. Expand
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities
TLDR
This paper provides a decentralized coordination framework for CAVs at a signal-free intersection to minimize travel time and improve fuel efficiency and integrates a first-in-first-out queuing policy to improve the performance of the system. Expand
A multiobjective optimization framework for stochastic control of complex systems
TLDR
This paper treats the stochastic control problem as a multiobjective optimization problem of the one-stage expected costs of the subsystems, and shows that the control policy yielding the Pareto optimal solution is an optimal control policy that minimizes the average cost criterion for the entire system. Expand
...
1
2
3
4
5
...