A novel observer-based neural-network finite-time output control for high-order uncertain nonlinear systems
Open accessAbstract
Due to the difficulty encountered in dealing with unstructured system dynamics with unmeasured system state variables, this paper presents a novel observer-based neural network finite-time output control strategy for general high-order nonlinear systems (HNSs). The suggested technique is performed based on the backstepping-like control (BSC) scheme with a hybrid nonlinear disturbance-state observer and norm estimation-based radial basis function neural network (RBFNN). This helps not only reduce the number of estimated parameters but also relax the restriction of using inequality when exploiting the norm estimation concept in a conventional way; thus, retaining the same properties of the original system. Therefore, an observer-based finite-time output feedback control is established to deal with the unstructured dynamical behaviors and satisfying the output tracking regulation with the semi-global practically finite-time stability (SGPFS) guaranteed for the closed-loop system. The effectiveness and workability of the proposed algorithm is verified by a numerical simulation on a specific practical application.
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- Authors
- Truong, H. V. A., Phan, V. D., Tran, D. T., & Ahn, K. K.
- Affiliations
- Pohang University of Science and Technology (KR)
- Vinh University (VN)
- Ho Chi Minh City University of Technology and Engineering (VN)
- University of Ulsan (KR)
- Corresponding author
- Ahn, Kyoung Kwan
- Venue
- Applied Mathematics and Computation
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 475
- Pages
- 128699
- ISSN
- 0096-3003 (ISSN-L)
- OpenAlex
- W4394870859
- Cited by
- 16
- References
- 48
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Applied Mathematics and Computation (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Research Foundation of Korea
- Ministry of Education
- Ministry of Education - Singapore (2021RIS-003)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Neural Networks Stability and Synchronization
- Keywords
- high-order nonlinear systems, uncertain nonlinear systems, radial basis function neural network, norm estimation, output tracking, unmeasured states, output feedback control
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