NDLI: Adaptive single neural network control for a class of uncertain discrete-time nonlinear strict-feedback systems with input saturation

Content Provider	Springer Nature Link
Author	Wang, Xin Liu, Zhengjiang Cai, Yao
Copyright Year	2015
Abstract	In this paper, a novel adaptive neural network control approach is presented for a class of uncertain discrete-time nonlinear strict-feedback systems with input saturation. By combining single neural network approximation and minimal learning parameter technique, the proposed approach is able to eliminate the complexity growing problem and alleviate the explosion of learning parameters. An auxiliary design system is incorporated into the control scheme to overcome the problem of input saturation constraints. Following this approach, the designed controller contains only one actual control law and one adaptive law, the numbers of input variables and weights of neural network updated online are decreased drastically, and the number of parameter updated online for whole system is reduced to only one. Compared with the existing methods, the adaptive mechanism with much simpler controller structure and minimal learning parameterization is achieved; therefore, the computational burden is lighter. It is shown via Lyapunov theory that all signals in the closed-loop system are uniformly ultimately bounded. Finally, simulation results via two examples are employed to illustrate the effectiveness and merits of the proposed scheme.
Starting Page	2021
Ending Page	2030
Page Count	10
File Format	PDF
ISSN	0924090X
Journal	Nonlinear Dynamics
Volume Number	82
Issue Number	4
e-ISSN	1573269X
Language	English
Publisher	Springer Netherlands
Publisher Date	2015-08-02
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discrete-time systems Adaptive control Backstepping design Single neural network Minimal learning parameter Input saturation constraints Vibration, Dynamical Systems, Control Mechanics Mechanical Engineering Automotive Engineering
Content Type	Text
Resource Type	Article
Subject	Ocean Engineering Applied Mathematics Control and Systems Engineering Mechanical Engineering Electrical and Electronic Engineering Aerospace Engineering

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