Online Adaptive Compensation for Model Uncertainty Using Extreme Learning Machine-based Control Barrier Functions-Reference-Cited by-同舟云学术

Online Adaptive Compensation for Model Uncertainty Using Extreme Learning Machine-based Control Barrier Functions

Published:2022-10-23 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Author:

Munoz Emanuel¹,Kalaria Dvij²,Lin Qin³,Dolan John M.¹

Affiliation:

1. Robotics Institute, Carnegie Mellon University

2. IIT Kharagpur,Department of Computer Science and Engineering,India

3. Cleveland State University,Electrical Engineering and Computer Science Department

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9981026/9981028/09981680.pdf?arnumber=9981680

Reference22 articles.

1. Reinforcement learning for safety-critical control under model uncertainty, using controllyapunov functions and control barrier functions;choi;ArXiv Preprint,2020

2. Robust safety-critical control for dynamic robotics;nguyen;IEEE Transactions on Automatic Control,2021

3. Gaussian process-based min-norm stabilizing controller for controlaffine systems with uncertain input effects and dynamics;castaneda;2021 American Control Conference (ACC),0

4. End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

5. Control barrier function based quadratic programs with application to adaptive cruise control

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1. Robust Control Barrier Functions for Safe Control Under Uncertainty Using Extended State Observer and Output Measurement;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13