This project is developing tools for traffic management and control using formal methods. By applying techniques such as model-checking and correct-byconstruction synthesis, we ensure that traffic flow satisfies high-level objectives expressed using temporal logics that guarantee desirable behavior such as avoiding congestion, maintaining high throughput, ensuring fairness of ramp metering strategies, and reacting to incidents or unexpected conditions.

The purpose of this project is to develop, simulate and test through targeted vehicle and roadway infrastructure field test experiments a traffic operating system  (TOS)  that organizes existing computation, communication  and  automotive  technologies to minimize congestion by increasing traffic throughput and to enhance safety by reducing driver errors through the use of cooperative  adaptive  cruise  control  strategies.  These goals are achieved through integration of traffic measurements with the&

Optimal Power Flow (OPF) is a crucial part of the Energy Management System (EMS) as it determines individual generator outputs that minimize generation cost while satisfying transmission, generation, and system level operating constraints. OPF relies on a core EMS routine, namely state estimation, which computes system states, principally bus voltages/phase angles at the buses. However, state estimation is vulnerable to false data injection attacks in which an adversary can alter certain measurements to corrupt the estimator’s solution without being detected.

Wireless sensor-actuator networks (WSANs) are designed to collect and disseminate information using a physically distributed collection of wireless nodes and multi-hop protocols. WSANs are gaining rapid adoption in industrial automation and manufacturing applications due to their low deployment cost, robustness, and configuration flexibility. While the early success of industrial WSANs has been focused on monitoring applications, there are significant advantages, and also challenges, when WSANs are used in feedback control applications.

Wireless sensor-actuator networks (WSANs) are designed to collect and disseminate information using a physically distributed collection of wireless nodes and multi-hop protocols. WSANs are gaining rapid adoption in industrial automation and manufacturing applications due to their low deployment cost, robustness, and configuration flexibility. While the early success of industrial WSANs has been focused on monitoring applications, there are significant advantages, and also challenges, when WSANs are used in feedback control applications.

This project aims to investigate energy and delay sensitive sensing, communication, decision-making and control in the context of cyber physical systems. We particularly focus on systems where sensors with varying accuracy observe heterogeneous source signals that must be processed and communicated and used for inference and decision-making purposes.

A challenge for assisting young developmentally-delayed children in learning to walk is to (1) stabilize medio-lateral body sway while promoting opportunities for exploratory behavior, and (2) develop gait that exploits exchange of potential and kinetic energy. To meet the challenge, we have built a modular multi-robot CPS: a scaffold that applies forces at the pelvis via cables to modulate and stabilize center of mass behavior, and a wearable robot that applies assistive torques at the hip.

The proposed research focuses on innovation in the area of detection and separation of anomaly in sensory data used for real time monitoring and control of cyber-physical systems (CPS). In this work, bulk power grid is considered as an example CPS, which is a critical infrastructure of our nation. The problem of spurious or maliciously injected sensor data originating from cyber-attacks is very important, because it can seriously jeopardize the monitoring and stabilization controls of power grids. This can lead to system-wide blackouts and cost our economy billions of dollars.