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Multi-H∞ controls for unknown input-interference nonlinear system with reinforcement learning

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Lv, Yongfeng, Na, Jing, Zhao, Xiaowei, Huang, Yingbo and Ren, Xuemei (2022) Multi-H∞ controls for unknown input-interference nonlinear system with reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems . pp. 1-13. doi:10.1109/TNNLS.2021.3130092 (In Press)

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Official URL: http://dx.doi.org/10.1109/TNNLS.2021.3130092

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Abstract

This article studies the multi-H∞ controls for the input-interference nonlinear systems via adaptive dynamic programming (ADP) method, which allows for multiple inputs to have the individual selfish component of the strategy to resist weighted interference. In this line, the ADP scheme is used to learn the Nash-optimization solutions of the input-interference nonlinear system such that multiple H∞ performance indices can reach the defined Nash equilibrium. First, the input-interference nonlinear system is given and the Nash equilibrium is defined. An adaptive neural network (NN) observer is introduced to identify the input-interference nonlinear dynamics. Then, the critic NNs are used to learn the multiple H∞ performance indices. A novel adaptive law is designed to update the critic NN weights by minimizing the Hamiltonian-Jacobi-Isaacs (HJI) equation, which can be used to directly calculate the multi-H∞ controls effectively by using input-output data such that the actor structure is avoided. Moreover, the control system stability and updated parameter convergence are proved. Finally, two numerical examples are simulated to verify the proposed ADP scheme for the input-interference nonlinear system.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > T Technology (General)
Divisions: Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH): Reinforcement learning , Dynamic programming, Neural networks (Computer science) , Nonlinear systems
Journal or Publication Title: IEEE Transactions on Neural Networks and Learning Systems
Publisher: IEEE
ISSN: 2162-237X
Official Date: 2022
Dates:
DateEvent
2022Published
7 December 2021Available
17 November 2021Accepted
Page Range: pp. 1-13
DOI: 10.1109/TNNLS.2021.3130092
Status: Peer Reviewed
Publication Status: In Press
Publisher Statement: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/S001905/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
62103296[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61922037[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61873115[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
62003153[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
61973036[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809

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