Investigating Integral Reinforcement Learning to Achieve Asymptotic Stability in Underactuated Mechanical Systems-Reference-Cited by-同舟云学术

Investigating Integral Reinforcement Learning to Achieve Asymptotic Stability in Underactuated Mechanical Systems

Published:2024-01 Issue:1 Volume:9 Page:191-198
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Salamat Babak¹^ORCID,Bencic Daniel¹^ORCID,Elsbacher Gerhard¹^ORCID,Seidel Christian¹^ORCID,Tonello Andrea M.²^ORCID

Affiliation:

1. AImotion Institute, Technische Hochschule Ingolstadt, Ingolstadt, Germany

2. Institute of Networked and Embedded Systems, Alpen-Adria University Klagenfurt, Klagenfurt, Austria

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/10320104/10316576.pdf?arnumber=10316576

Reference29 articles.

1. Learning-Based Balance Control of Wheel-Legged Robots

2. Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances

3. Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problem

4. Online actor critic algorithm to solve the continuous-time infinite horizon optimal control problem

5. Generalized value iteration for discounted optimal control with stability analysis

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