CaltechAUTHORS
Published December 14, 2020 | Version Submitted
Book Section - Chapter Open

Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning

Creators

  • 1. ROR icon University of California, Berkeley

Abstract

The following topics are dealt with: control system synthesis; nonlinear control systems; linear systems; stability; optimisation; feedback; closed loop systems; Lyapunov methods; multi-agent systems; optimal control.

Additional Information

© 2020 IEEE. This work is funded by HICON-LEARN (design of HIgh CONfidence LEARNing-enabled systems), Defense Advanced Research Projects Agency award number FA8750-18-C-0101, and Provable High Confidence Human Robot Interactions, Office of Naval Research award number N00014-19-1-2066.

Attached Files

Submitted - 2004.02766.pdf

Files

2004.02766.pdf

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Additional details

Additional titles

Alternative title
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning

Identifiers

Eprint ID
110732
Resolver ID
CaltechAUTHORS:20210903-222215502

Related works

Describes
10.1109/cdc42340.2020.9304242 (DOI)
https://arxiv.org/abs/2004.02766 (URL)

Funding

Defense Advanced Research Projects Agency (DARPA)
FA8750-18-C-0101
Office of Naval Research (ONR)
N00014-19-1-2066

Dates

Created
2021-09-07
Created from EPrint's datestamp field
Updated
2021-09-07
Created from EPrint's last_modified field