Published December 14, 2020
| Submitted
Book Section - Chapter
Open
Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
Chicago
An error occurred while generating the citation.
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
Files
(477.8 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:9586d31615a9b92cb34d5683a0db475f
|
477.8 kB | Preview Download |
Additional details
- Alternative title
- Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
- Eprint ID
- 110732
- Resolver ID
- CaltechAUTHORS:20210903-222215502
- Defense Advanced Research Projects Agency (DARPA)
- FA8750-18-C-0101
- Office of Naval Research (ONR)
- N00014-19-1-2066
- Created
-
2021-09-07Created from EPrint's datestamp field
- Updated
-
2021-09-07Created from EPrint's last_modified field