The formalization of system engineering models and approaches.
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The goal of this project is to create an integrative framework for the design of coupled biological and robotic systems that accommodates system uncertainties and competing objectives in a rigorous, holistic, and effective manner. The design principles are developed using a concrete, end-to-end application of tracking and modeling fish movement with a network of gliding robotic fish. The proposed robotic platform is an energy-efficient underwater gliding robotic fish that travels by changing its buoyancy and mass distribution (gliding) or by flapping tail fin (swimming).
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The project aims to advance both foundations and enabling technologies in the field of human-machine systems, with a focus on exercise and rehabilitation machines. A human interacting with an advanced (actively-controlled) exercise machine is the ultimate cyber-physical system due to the presence of multi-level loop closures, large-scale, coupled musculoskeletal dynamics, conflicting objectives between human vs. machine controllers, uncertain dynamics and limited sensing.
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This poster surveys results obtained by the project team in the area of logical characterizations for bisimulation over a general mathematical model for cyber-physical systems (CPSs) called Generalized Synchronization Trees (GSTs). GSTs extend traditional models for discrete-event systems with capabilities for modeling non-discrete behavior, and are intended to serve as a vehicle for giving mathematically well-defined notions of compositions for CPSs. Bisimulation represents a notion of equivalence over GSTs that captures when two GSTs are indistinguishable to an outside observer.
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Smart grid includes two interdependent infrastructures: power transmission and distribution network, and the supporting telecommunications network. Complex interactions among these infrastructures lead to new pathways for attack and failure propagation that are currently not well understood. This innovative project takes a holistic multilevel approach to understand and characterize the interdependencies between these two infrastructures, and devise mechanisms to enhance their robustness.
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Cyber-Physical Systems (CPS) are being increasingly deployed in critical infrastructures such as electric-power, water, transportation, and other networks. These deployments are facilitating real-time monitoring and closed-loop control by exploiting the advances in wireless sensor-actuator networks, the internet of "everything," data-driven analytics, and machine-to-machine interfaces. CPS operations depend on the synergy of computational and physical components.
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The overarching project goal is to advance the design of opportunistic state-triggered aperiodic controllers for networked cyber-physical systems. This poster considers the problem of opportunistic human-robot collaboration to solve multi-objective optimization problems. We consider scenarios where a human decision maker works with a robot in a supervisory manner in order to find the best Pareto solution to a given optimization problem. The human has a time-invariant function that represents the value she gives to the different outcomes.
