Distributed and Robust Estimation for Cyberphysical Systems using a Nonlinear Consensus Approach Nonlinear Robust Distributed Consensus for Cyberphysical Systems Cyberphysical systems such as photovoltaic arrays are complex systems that need continuous monitoring. These systems also occupy large areas and rely on wireless sensor platforms to measure and monitor the state of the systems. Large numbers of sensors are often deployed, and cannot tranmit over large distances due to power limitations. Furthermore, due to the presence of a large number of transmitters, the system is subject to impulsive noise and network interference, which are typically non-Gaussian and heavy-tailed. It is therefore imperative for the distributed systems to be fully decentralized and at the same time, robust to a wide variety of sensing, and channel impairments. This necessitates the joint design of the sensing apparatus and the communication system while respecting low-power constraints. A robust statistic of the sensed data as well as one which satisfies peak-power and computational requirements are required. The marriage between robust statistics which traditionally works with nonlinear operations of ranks and signs of the data, and transmit power constraints for communications, sparks a transformative connection between statistics and communication theory. We propose to design a suite of robust, low-power implementable distributed estimation algorithms for fully distributed consensus systems without fusion centers with the following thrusts: (T1) Distributed Average Consensus using Bounded Transmissions: In contrast to sensor networks with a fusion center (FC) or central server, fully distributed sensor systems which rely on consensus-based methods are robust to node failure. (T2) Robustness to Noise and Outliers: The presence of outliers and noise in the sensing stage requires robustness to noise and the presence of outliers due to network interference from randomly scattered nodes which typically cause heavy-tailed impulsive interference, taking very large values with high probability. (T3) Applications to Photovoltaic Systems: The distributed algorithms will be applied to the PV area. Fault detection algorithms for solar arrays need to use the values supplied by the sensors to determine if a fault has occurred, and if a fault has occurred, what the nature of the fault is. (T4) Real-Time Testbed of Consensus Systems: A testbed will be developed using USRP2 software radios and will be used to verify and validate the algorithms developed for both simulated, as well as real PV data.
|Effective start/end date||9/1/13 → 8/31/18|
- National Science Foundation (NSF): $299,999.00
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