University of California, Riverside

Department of Electrical and Computer Engineering



Ren receives an NSF grant for distributed nonlinear multi-agent coordination in asymmetric switching networks


Ren receives an NSF grant for distributed nonlinear multi-agent coordination....
 
Ren

Ren receives an NSF grant for distributed nonlinear multi-agent coordination in asymmetric switching networks

August 9, 2013

EE Professor Wei Ren received a three-year, $397,053 NSF grant titled “Distributed Nonlinear Multi-agent Coordination in Asymmetric Switching Networks: A Sequential Comparison Framework”. The main objective of the proposed research is to derive a novel sequential comparison framework for distributed nonlinear multi-agent coordination in asymmetric switching networks. Multi-agent systems have numerous applications including environment monitoring, precision agriculture, search and rescue, space-based interferometry, and ocean sampling.

The basic idea is that the stability of one system can be inferred by that of another carefully chosen simpler system by suitably choosing scalar nonnegative functions for each system, comparing their derivatives along the trajectories of their corresponding systems, and exploiting structural similarity. It is not required that the nonnegative functions be non-increasing (with negative semidefinite derivatives or the alike) or show up in their derivatives, or one be upper bounded by another. Instead the structural relationship between them and their derivatives plays a role. The comparison can be performed sequentially with simpler and simpler systems until one final system whose stability can be obtained easily with a conventional method. The sequential comparison procedure is particularly promising for tackling the challenges in distributed nonlinear multi-agent coordination with significant complexity by sequentially reducing the complexity. The PI will address four challenging problems in asymmetric switching networks, namely, modular design and analysis in distributed control, fully distributed algorithm design with adaptive laws, distributed control of nonlinear passive systems, and distributed control of heterogeneous agents with unknown nonlinear dynamics.

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