Static Output-Feedback H∞ Control Design Procedures for Continuous-Time Systems With Different Levels of Model Knowledge

Shai A. Arogeti, Frank L. Lewis

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This article suggests a collection of model-based and model-free output-feedback optimal solutions to a general H control design criterion of a continuous-time linear system. The goal is to obtain a static output-feedback controller while the design criterion is formulated with an exponential term, divergent or convergent, depending on the designer's choice. Two offline policy-iteration algorithms are presented first, which form the foundations for a family of online off-policy designs. These algorithms cover all different cases of
partial or complete model knowledge and provide the designer with a collection
of design alternatives. It is shown that such a design for partial model
knowledge can reduce the number of unknown matrices to be solved online. In
particular, if the disturbance input matrix of the model is given, off-policy
learning can be done with no disturbance excitation. This alternative is useful
in situations where a measurable disturbance is not available in the learning
phase. The utility of these design procedures is demonstrated for the case of
an optimal lane tracking controller of an automated car.
Original languageEnglish
Pages (from-to)1432-1446
Number of pages15
JournalIEEE Transactions on Cybernetics
Volume53
Issue number3
DOIs
StatePublished - 1 Mar 2023

Keywords

  • Hoptimal control
  • off-policy reinforcement learning (RL)
  • static output feedback

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

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