Distributed State Estimation With Deep Neural Networks for Uncertain Nonlinear Systems Under Event-Triggered Communication

Federico M. Zegers, Runhan Sun, Girish Chowdhary, Warren E. Dixon

Research output: Contribution to journalArticlepeer-review

Abstract

This work explores the distributed state estimation problem for an uncertain, nonlinear, and continuous-time system. Given a sensor network, each agent is assigned a deep neural network (DNN) that is used to approximate the system's dynamics. Each agent updates the weights of their DNN through a multiple timescale approach, i.e., the outer layer weights are updated online with a Lyapunov-based gradient descent update law, and the inner layer weights are updated concurrently using a supervised learning strategy. To promote the efficient use of network resources, the distributed observer uses event-triggered communication. A nonsmooth Lyapunov analysis demonstrates that the distributed event-triggered observer achieves uniformly ultimately bounded state reconstruction. A simulation example of a five-agent sensor network estimating the state of a two-link robotic manipulator tracking a desired trajectory is provided to validate the result and showcase the performance improvements afforded by DNNs.

Original languageEnglish (US)
Pages (from-to)3107-3114
Number of pages8
JournalIEEE Transactions on Automatic Control
Volume68
Issue number5
DOIs
StatePublished - May 1 2023

Keywords

  • Lyapunov methods
  • Multi-agent systems
  • deep learning
  • nonlinear control systems
  • state estimation
  • wireless sensor networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

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