Fixed-time adaptive neural network compensation control for uncertain nonlinear systems
Abstract
Uncertainties are the main obstacle to improving the control performance of nonlinear systems. To address this challenge, this paper proposes a fixed-time adaptive neural network compensation control method for a class of high-order nonlinear systems exhibiting both uncertain nonlinearities and parametric uncertainties. Specifically, a fixed-time adaptive neural network (FTANN) is designed to deal with uncertain nonlinearities, while a new fixed-time adaptive law is developed to address parametric uncertainties. These two approaches are integrated with a gain-adaptive fixed-time filter within the dynamic surface control (DSC) framework. This not only resolves the "differential explosion" problem but also reduces the conservatism of the controller by lowering the robust feedback gain. As proven through Lyapunov analysis, the proposed controller guarantees fixed-time stability for all system states. Comparative simulations and experimental results provide further evidence supporting the effectiveness of the proposed controller.
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Neural networks have been successfully used to address a wide range of control problems involving unknown system dynamics.
“Additionally, leveraging the universal function approximation property of neural networks, researchers have successfully addressed a wide range of control problems involving unknown system dynamics [16,17].”
From Predefined-Time Tracking Control of Servo Hydraulic Cylinder Based on Reinforcement Learning (2025) · cites this paper as [16]
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- Authors
- Ma, J., Yao, Z., Deng, W., & Yao, J.
- Affiliations
- Nanjing University of Science and Technology (CN)
- Corresponding author
- Yao, Zhikai
- Yao, Jianyong
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2025-09
- Volume
- 189
- Pages
- 107563
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W4410350820
- Cited by
- 11
- References
- 43
- Citations by year
- 2026: 7 · 2025: 4
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (U24A20112, 52405065, 52275062)
- National Key Research and Development Program of China (2024YFB4709600)
- Natural Science Foundation of Jiangsu Province (BK20232038, BK20230096)
- 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 › Physics and Astronomy › Statistical and Nonlinear Physics › Chaos control and synchronization
- Keywords
- fixed-time control, uncertain nonlinear systems, parameter uncertainty, dynamic surface control, differential explosion, high-order nonlinear systems
- MeSH terms
- Algorithms, Computer Simulation, Feedback, Time Factors, Neural Networks, Computer, Nonlinear Dynamics, Uncertainty
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