Cyber-physical systems (CPSs) have become pervasive in the modern society, enabling transformative applications in the transportation, healthcare and energy sectors. However, the reliable development of CPSs remains an outstanding challenge. At the design level, hybrid systems theory provides a rich set of techniques and tools for ensuring correctness of high level functional properties such as safety and liveness. Current analysis techniques at the implementation level focus primarily on detecting low level runtime errors such as buffer overflows and divide by zero.

The vision of this work is to unite experts in granular mechanics, optimal control, and learning theory in order to define a methodology for advancing cyber-physical systems (CPS) involving a tight coupling of the physical with the cyber through dynamic interactions that must be learned online. The proposed work will advance the science of cyber-physical systems by more explicitly tying sensing, perception, and computing to the optimization and control of physical systems whose properties are variable and uncertain.

The major goal of this project is to investigate ecient, economic ways to integrate renewable energy resources.

Wireless sensor actuator networks (WSANs) are gaining rapid adoption in industrial automation and manufacturing due to their low deployment cost and flexibility. While early success of industrial WSANs focused on monitoring applications, there are significant advantages, and also challenges, in employing WSANs in feedback control applications, since they require careful design and testing of both network configurations as well as control algorithms.

Objective: The objective of this project is to improve the performance and current capabilities of automotive active safety control systems by taking into account the interactions between the driver, the vehicle, the active safety system and the environment. The current approach in the design of automotive active safety systems follows the philosophy "one size fits all," in the sense that active safety systems are the same for all vehicles and do not take into account the skills, habits and state of the human driver who may operate the vehicle. 

 

One of the challenges for the future cyber-physical systems is the exploration of large design spaces.  Evolutionary algorithms (EAs), which embody a simplified computational model of the mutation and selection mechanisms of natural evolution, are known to be effective for design optimization.  However, the traditional formulations are limited to choosing values for a predetermined set of parameters within a given fixed architecture.  This project explores techniques, based on the idea of hidden genes, which enable EAs to select a variable number of components, thereby expand

Visual identification of structural flaws is quite valuable not only to predict an imminent collapse of a bridge, but also to determine effective precautionary measures and repairs.