Fuzzy Weight-Based Reinforcement Learning for Event-Triggered Optimal Backstepping Control of Fractional-Order Nonlinear Systems

Li, Dongdong; Dong, Jie

doi:10.1109/tfuzz.2023.3294928

IEEE Trans. Fuzzy Syst.

2024

DOI: 10.1109/tfuzz.2023.3294928

|View full text |Cite

Fuzzy Weight-Based Reinforcement Learning for Event-Triggered Optimal Backstepping Control of Fractional-Order Nonlinear Systems

Dongdong Li

Jie Dong

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 17 publications

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Adaptive deep neural network optimized control for a class of nonlinear strict‐feedback systems with prescribed performance

Lu,

Wu,

Wang

2024

Adaptive Control & Signal

View full text Add to dashboard Cite

SummaryIn this article, an adaptive deep neural network (DNN) optimized control strategy is developed for a class of nonlinear strict‐feedback systems with prescribed performance. First, the DNN is applied to approximate the unknown function, and the weight update law is designed to reduce the mathematical challenge based on the first‐order Taylor's series. Second, the optimized backstepping technique is utilized to construct virtual and actual controllers in the backstepping process to achieve the overall control optimization of the system. Next, a control strategy based on the time‐varying switching function and the quartic barrier Lyapunov function is employed to achieve the prescribed performance. Then, the tracking error can converge to the prescribed accuracy within the prescribed time, and every signal within the system has a bound. Finally, the particle swarm optimization algorithm is utilized to search for the designed parameters and simulation examples to verify the effectiveness of the control strategy.

show abstract

Adaptive deep neural network optimized control for a class of nonlinear strict‐feedback systems with prescribed performance

Lu,

Wu,

Wang

2024

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

Safe optimal robust control of nonlinear systems with asymmetric input constraints using reinforcement learning

Zhang,

Wang,

Jiang

et al. 2023

Appl Intell

View full text Add to dashboard Cite

E-DBRL: efficient double broad reinforcement learning for adaptive traffic signal control

Deng,

Yin,

Pei

et al. 2024

Appl Intell

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Fuzzy Weight-Based Reinforcement Learning for Event-Triggered Optimal Backstepping Control of Fractional-Order Nonlinear Systems

Cited by 17 publications

References 52 publications

Adaptive deep neural network optimized control for a class of nonlinear strict‐feedback systems with prescribed performance

Adaptive deep neural network optimized control for a class of nonlinear strict‐feedback systems with prescribed performance

Safe optimal robust control of nonlinear systems with asymmetric input constraints using reinforcement learning

E-DBRL: efficient double broad reinforcement learning for adaptive traffic signal control

Contact Info

Product

Resources

About