Tang, Z., Rossiter, J.A. orcid.org/0000-0002-1336-0633, Dong, Y. et al. (1 more author) (2024) Reinforcement learning-based output stabilization control for nonlinear systems with generalized disturbances. In: 2024 IEEE International Conference on Industrial Technology (ICIT). 2024 IEEE International Conference on Industrial Technology (ICIT), 25-27 Mar 2024, Bristol, United Kingdom. Institute of Electrical and Electronics Engineers , pp. 1-6. ISBN 979-8-3503-4026-6
Abstract
This paper proposes a new disturbance observer (DO)-based reinforcement learning (RL) control approach for nonlinear systems with unmatched (generalized) disturbances. While a nonlinear disturbance observer (NDO) is utilized to measure the plant uncertainties, disturbances can exist in the plant via distinct channels from those of the control signals; so-called mismatched disturbances are theoretically difficult to attenuate within the channel of the system's states. A generalized disturbance observer-based compensator is implemented to address the uncertainty cancellation problem by removing the influence of uncertainties from the output channels. Con-currently, a composite actor-critic RL scheme is utilized for approximating the optimal control policy as well as the ideal value function pertaining to the compensated system by solving a Hamilton-Jacobi-Bellman (HJB) equation for both online and offline iterations simultaneously. Stability analysis verifies the convergence of the proposed framework. Simulation results are included to illustrate the effectiveness of the proposed scheme.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2024 IEEE International Conference on Industrial Technology (ICIT) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Uncertainty; Simulation; Measurement uncertainty; Optimal control; Estimation; Reinforcement learning; Disturbance observers |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/V051261/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Nov 2024 09:42 |
Last Modified: | 27 Nov 2024 09:42 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/icit58233.2024.10540609 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220144 |