One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Genetic algorithms (GAs), 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.

Abstract: As part of our CPS project, we have focused on the problem of model repair for cyber-physical systems. This work involves identifying constraints caused due to physical components during revision. We consider four types of constraints cyber-cyber, cyber-physical, physical-cyber and physical-physical. Based on the complexity limitations caused by these constraints we are developing efficient heuristics to mitigate the cost of model repair. We have also focused on extending revision to code level.

Abstract: One of the challenges for the future cyber-physical systems is the exploration of large design spaces. Genetic algorithms (GAs), which embody a simplified computational model of the mutation and election 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.

Abstract: Success of numerous long-term robotic network missions in space, air, ground, and water is measured by the ability of the robots to operate for extended time in highly dynamic and potentially hazardous operating environments. The proposed work responds to the urgency for development of innovative mobile power distribution systems that lower deployment and operating costs, while simultaneously increasing mission efficiency, and supporting the network’s need to be responsive to changing physical conditions.