Stochastic Control Approach for Distributed Generation Units Interacting on Graphs

Nezar Alyazidi, Magdi S. Mahmoud, Mohammed Abouheaf, Adel Sharaf

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

This chapter introduces a novel online adaptive learning distributed control approach for a system of distributed generation units with disturbances in their dynamical environments. The interactions between the generation units are restricted by a graph topology, which means that the dynamics of the generation units are coupled. The coordination ideas or distributed synchronization protocols are utilized to maintain synchronization among the generation units. The cost function is designed to take into account the neighborhood interactions and the graph topology. Coupled Bellman optimality equations are developed for the distributed generation network. A distributed online reinforcement learning approach that employs a Kalman filter is employed to solve the optimal control problem of the multiagent system. This control approach is implemented in real time using neural network approximations and it does not need to know the full dynamics of the generation systems. The validity of the distributed control approach is tested using a system of distributed generation units working under disturbances.

Original languageEnglish
Title of host publicationNew Trends in Observer-based Control
Subtitle of host publicationA Practical Guide to Process and Engineering Applications
PublisherElsevier
Pages77-98
Number of pages22
ISBN (Electronic)9780128170342
ISBN (Print)9780128170359
DOIs
StatePublished - 1 Jan 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Inc. All rights reserved.

Keywords

  • Adaptive critics
  • Adaptive learning
  • Cooperative control
  • Distributed generation units
  • Kalman filtering
  • Optimal control
  • Policy iteration

ASJC Scopus subject areas

  • General Engineering

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