Neural Adaptive Dynamic Surface Asymptotic Tracking Control for a Class of Uncertain Nonlinear System
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Neural Adaptive Dynamic Surface Asymptotic Tracking Control for a Class of Uncertain Nonlinear System Jiacheng Song1 · Maode Yan1 · Panpan Yang1 Received: 28 September 2019 / Revised: 14 September 2020 / Accepted: 21 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In this paper, by incorporating the neural network into an adaptive dynamic surface control (DSC) framework, a neural adaptive DSC algorithm is developed for a class of uncertain nonlinear system to ensure the asymptotic output tracking. Neural network is used to approximate the unknown nonlinear term in the system such that the requirements for known nonlinear term in control laws design procedure are released. In order to eliminate the boundary layer effects, which are caused by the linear filters at each step in the DSC procedure, the nonlinear filters with the compensation term are designed skillfully. The proposed neural adaptive DSC algorithm not only avoids the inherent problem of “explosion of complexity” in the backstepping procedure, but also has its own advantages: (1) releasing the requirements for known nonlinear term in control laws design procedure; (2) holding the asymptotic output tracking performance. Some simulations are shown to demonstrate the effectiveness and advantages of the proposed controller. Keywords Neural network · Dynamic surface control · Adaptive control · Asymptotic output tracking · Uncertain nonlinear system
1 Introduction In past decades, nonlinear control design has been a hot research field due to its better control effects than linear control [9,33,37]. Meanwhile, quite a few methods were proposed to design stable and reliable controllers for nonlinear systems, such as
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Maode Yan [email protected] Jiacheng Song [email protected] Panpan Yang [email protected]
1
School of Electronic and Control Engineering, Chang’an University, Xi’an, China
Circuits, Systems, and Signal Processing
sliding-mode control, feedback linearization, backstepping method, adaptive control, neural network and fuzzy logical control [2,3,7,11,19,35,39,40]. Adaptive control is an effective and significant method to solve the uncertainty of parameters in the nonlinear system, and has been well studied from different perspectives in terms of handling constrained problems (input saturation, state immeasurable, etc.) and improving the control performance [5,18,36]. However, for a nonlinear system with unknown nonlinear term, which not only the uncertainty of parameters, the simple adaptive control cannot guarantee the desired and ideal control performance. Recently, by incorporating the adaptive control technique into neural network framework, the neural networkbased adaptive method is developed to promote the control performance for a class of uncertain nonlinear system, where the nonlinear term is an unknown function [30,34]. Backstepping method is an efficient and powerful approach for high-order nonlinear systems from the viewpoint of the theoretical analysis and the engineering app
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