Adaptive Neural Network Synergetic Control for Nonlinear Hysteretic Systems.

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Bibliographic Details
Title:	Adaptive Neural Network Synergetic Control for Nonlinear Hysteretic Systems.
Authors:	Zhang, Wenhui¹ hit_zwh@126.com, Zhou, Hui² 15261498478@163.com, Chen, Rui³ cr1716707642@126.com, Ma, Jing⁴ mj2201@126.com
Source:	IAENG International Journal of Applied Mathematics. Sep2026, Vol. 56 Issue 9, p4019-4023. 5p.
Subjects:	Artificial neural networks, Nonlinear systems, Manipulators (Machinery), Hysteresis, Radial basis functions, Adaptive control systems, Tracking control systems
Abstract:	This paper proposes an adaptive neural network-based synergetic control scheme for nonlinear systems with unknown input delays and modeling uncertainties. Radial basis function (RBF) neural networks are employed to approximate unknown dynamics online, reducing the effects of uncertainties and delay-induced disturbances. A macro-variable-based synergetic manifold is designed to improve tracking convergence, while adaptive laws update the network weights online without prior knowledge of system nonlinearities. Lyapunov analysis guarantees the boundedness of all closed-loop signals and the convergence of the tracking error. Simulations on a single-link robotic manipulator verify the effectiveness of the proposed method. Comparative results demonstrate improved tracking accuracy, faster transient response, and stronger robustness than adaptive backstepping control. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This paper proposes an adaptive neural network-based synergetic control scheme for nonlinear systems with unknown input delays and modeling uncertainties. Radial basis function (RBF) neural networks are employed to approximate unknown dynamics online, reducing the effects of uncertainties and delay-induced disturbances. A macro-variable-based synergetic manifold is designed to improve tracking convergence, while adaptive laws update the network weights online without prior knowledge of system nonlinearities. Lyapunov analysis guarantees the boundedness of all closed-loop signals and the convergence of the tracking error. Simulations on a single-link robotic manipulator verify the effectiveness of the proposed method. Comparative results demonstrate improved tracking accuracy, faster transient response, and stronger robustness than adaptive backstepping control. [ABSTRACT FROM AUTHOR]
ISSN:	19929978