This project is a component of a larger effort is to develop the foundations of modeling, synthesis and development of verified medical device software and systems from verified closed-loop models of the device and organ(s). This research spans both implantable medical devices such as cardiac pacemakers and physiological control systems such as drug infusion pumps which have multiple networked medical systems. Here we focus on an education and outreach activity associated with the project.

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).

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.

Episodic brain disorders such as epilepsy have a considerable impact on a patient’s productivity and quality of life and may be life-threatening when seizures cannot be controlled with medications. We will create a second generation brain-implantable sensing and stimulating device (BISSD) based on CPS principles and practice. The BISSD will be composed of modules placed intracranially to continuously monitor brain state and vulnerability to seizure and intervene with electrical stimulation to block the development of seizure.

To improve the current capabilities of automotive active safety control systems (ASCS) one needs to take into account the interactions between driver/vehicle/ASCS/environment. To achieve this goal, we are proposing a novel approach to collect data from a sensor-equipped vehicle. Motion Sensors (Inertial Measurement Units) are placed on various locations in the car, particularly around the driver’s operational environment and moving car components, such as steering wheel, seat, pedals, as well as critical car components (e.g. motor, suspensions).

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.