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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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WRAP-Multi-H∞-controls-unknown-inputs-nonlinear-reinforcement-learning-2021.pdf - Accepted Version - Requires a PDF viewer. Download (1589Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TNNLS.2021.3130092
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 | |||||||||||||||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > T Technology (General) |
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| 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: |
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| 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: |
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