Quantized Input Robust Adaptive Neural Network Control for Nonlinear Systems With Full State Constraints-Reference-Cited by-同舟云学术

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Quantized Input Robust Adaptive Neural Network Control for Nonlinear Systems With Full State Constraints

Published:2024 Issue: Volume:12 Page:121407-121415
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yang Qiyao¹^ORCID,He Zhongjie²^ORCID,Cai Jianping¹^ORCID,Yan Qiuzhen³,Mei Congli¹,Zhang Haibo³^ORCID

Affiliation:

1. Nanxun Innovation Institute, Zhejiang University of Water Resources and Electric Power, Hangzhou, China

2. School of Automation, Hangzhou Dianzi University, Hangzhou, China

3. College of Information Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou, Zhejiang, China

Funder

National Natural Science Foundation of China

Nanxun Scholars Program of Zhejiang University of Water Resources and Electric Power

ZJWEU Research Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10659894.pdf?arnumber=10659894

Reference32 articles.

1. Linear systems with state and control constraints: the theory and application of maximal output admissible sets

2. Constrained model predictive control: Stability and optimality

3. Reference Governor for Constrained Systems with Time-varying References

4. Convex optimization based iterative learning control for iteration-varying systems under output constraints

5. Neuroadaptive Control for Active Suspension Systems With Time-Varying Motion Constraints: A Feasibility-Condition-Free Method

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