Evolving Gaussian Processes and Kernel Observers for Learning and Control in Spatiotemporally Varying Domains: With Applications in Agriculture, Weather Monitoring, and Fluid Dynamics

Joshua E. Whitman, Harshal Maske, Hassan A. Kingravi, Girish Chowdhary

Research output: Contribution to journalArticlepeer-review

Abstract

Monitoring and modeling large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution by using a network of distributed sensors is a critical problem in many control applications (see 'Summary'). Consider, for example, a team of robots that has the task of destroying herbicide-resistant weeds on a farm (see Figure 1 and 'Key Control Problems in Agriculture'). This team must predict weed growth across the whole farm to make intelligent, coordinated decisions [1].

Original languageEnglish (US)
Article number9329182
Pages (from-to)30-69
Number of pages40
JournalIEEE Control Systems
Volume41
Issue number1
DOIs
StatePublished - Feb 2021

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Electrical and Electronic Engineering

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