Improved off‐policy reinforcement learning algorithm for robust control of unmodeled nonlinear system with asymmetric state constraints-Reference-Cited by-同舟云学术

Improved off‐policy reinforcement learning algorithm for robust control of unmodeled nonlinear system with asymmetric state constraints

Published:2022-10-25 Issue:3 Volume:33 Page:1607-1632
ISSN:1049-8923
Container-title:International Journal of Robust and Nonlinear Control
language:en
Short-container-title:Intl J Robust & Nonlinear

Author:

Zhang Yong¹^ORCID,Mu Chaoxu¹,Feng Yanghe²,Zhao Zhijia³^ORCID

Affiliation:

1. School of Electrical and Information Engineering Tianjin University Tianjin China

2. College of Systems Engineering National University of Defense Technology Changsha China

3. School of Mechanical and Electrical Engineering Guangzhou University Guangzhou China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Tianjin City

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Aerospace Engineering,Biomedical Engineering,General Chemical Engineering,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rnc.6432

Reference74 articles.

1. Barrier Lyapunov Functions for the control of output-constrained nonlinear systems

2. Control of nonlinear systems with partial state constraints using a barrier Lyapunov function

3. Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint

4. Backstepping dynamic surface control for a class of non‐linear systems with time‐varying output constraints

5. Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints

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