Adaptive Neural Network Control Design for Uncertain Nonstrict Feedback Nonlinear System With State Constraints
Abstract
In this paper, an adaptive neural network (NN) constraint control method is studied for a class of uncertain nonlinear nonstrict feedback systems with state constraints. The restrictive assumption that the unknown internal dynamics must possess the monotonically increasing characteristics in previous results is removed. The property of radial basis function (RBF)NNs is used to solve the algebraic loop problem based on the approximation structure. In order to achieve full state constraint satisfactions, the barrier Lyapunov functions (BLFs) are employed in each design procedure. Based on the backstepping and less adjustable parameters techniques, the controllers and the adaptive laws are obtained. By using the Lyapunov stability theory, the boundedness of all signals in the closed-loop system is proved. Therefore, the scheme not only solves the stability problem of the nonstrict feedback system but also overcomes the influence of the state constraint on the control performance. Finally, the effectiveness of the control method is verified by two simulation examples.
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Radial basis function neural networks with Gaussian function as an activation functions satisfying 0 < P_i^T(•)P_i(•) ≤ N have been implemented to manage such complexities.
“To address this challenge, radial basis function neural networks with Gaussian function as an activation functions satisfying 0 < P T i (•)P i (•) ≤ N have been implemented to manage such complexities [28,47,49].”
From Fixed-Time Adaptive Control for Nonstrict-Feedback Nonlinear Systems with Input Delay and Unknown Backlash-Like Hysteresis (2025) · cites this paper as [49]
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“To address this challenge, radial basis function neural networks with Gaussian function as an activation functions satisfying 0 P Ti ( · ) P i ( · ) ≤ N have been implemented to manage such complexities [28, 47, 49].”
- Authors
- Ma, L., & Liu, L.
- Affiliations
- Liaoning University of Technology (CN)
- Venue
- IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Published
- 2021-06
- Volume
- 51
- Issue
- 6
- Pages
- 3678-3686
- ISSN
- 2168-2216 (ISSN-L)
- 2168-2232
- OpenAlex
- W2957592576
- Cited by
- 79
- References
- 54
- Citations by year
- 2026: 10 · 2025: 18 · 2024: 28 · 2023: 8 · 2022: 4 · 2021: 8 · 2020: 3
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Systems Man and Cybernetics Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China (61603164, 61622303, 61773188, 61803189, 61803190, 61751202, 61572540, U1813203)
- Program for Liaoning Innovative Research Team in University (LT2016006)
- Fundamental Research Funds for the Universities of Liaoning Province (JZL201715402)
- 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
- adaptive neural network control, nonstrict-feedback nonlinear systems, state constraints, barrier Lyapunov function, radial basis function neural network, backstepping control, uncertain nonlinear systems, closed-loop stability
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