Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis Input

Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis Input

Most existing results do not take the effects of backlash hysteresis of actuators into account in a controller design of missile systems, but such hysteresis seems inevitable in practice. In this paper, a robust adaptive neural network (NN) control law for a missile system with unknown parameters an...

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Bibliographic Details
Main Authors: Jian-Ping Cai, Lantao Xing, Meng Zhang, Lujuan Shen
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Missle system
adaptive control
neural network
hysteresis
autopilot
aerodynamics
Online Access:https://ieeexplore.ieee.org/document/7982596/
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spelling doaj-59eead2a369d49149806c132a5f4d1382021-03-29T20:02:44ZengIEEEIEEE Access2169-35362017-01-015158391584710.1109/ACCESS.2017.27261867982596Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis InputJian-Ping Cai0https://orcid.org/0000-0003-4724-796XLantao Xing1Meng Zhang2Lujuan Shen3Zhejiang University of Water Resources and Electric Power, Hangzhou, ChinaState Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, ChinaState Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, ChinaZhejiang University of Water Resources and Electric Power, Hangzhou, ChinaMost existing results do not take the effects of backlash hysteresis of actuators into account in a controller design of missile systems, but such hysteresis seems inevitable in practice. In this paper, a robust adaptive neural network (NN) control law for a missile system with unknown parameters and hysteresis input is proposed based on a backstepping technique. The controller is designed by introducing NN approximation, which can be adjusted by an adaptive law based on the backstepping approach. The developed NN controller does not require a priori knowledge of the unknown backlash hysteresis. In particular, unlike existing results on adaptive compensation for unknown backlash hysteresis, the sign of b is no longer needed. It is shown that the designed controller can ensure the stability and tracking performance of the closed-loop system.https://ieeexplore.ieee.org/document/7982596/Missle systemadaptive controlneural networkhysteresisautopilotaerodynamics
collection DOAJ
language English
format Article
sources DOAJ
author Jian-Ping Cai
Lantao Xing
Meng Zhang
Lujuan Shen
spellingShingle Jian-Ping Cai
Lantao Xing
Meng Zhang
Lujuan Shen
Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis Input
IEEE Access
Missle system
adaptive control
neural network
hysteresis
autopilot
aerodynamics
author_facet Jian-Ping Cai
Lantao Xing
Meng Zhang
Lujuan Shen
author_sort Jian-Ping Cai
title Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis Input
title_short Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis Input
title_full Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis Input
title_fullStr Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis Input
title_full_unstemmed Adaptive Neural Network Control for Missile Systems With Unknown Hysteresis Input
title_sort adaptive neural network control for missile systems with unknown hysteresis input
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2017-01-01
description Most existing results do not take the effects of backlash hysteresis of actuators into account in a controller design of missile systems, but such hysteresis seems inevitable in practice. In this paper, a robust adaptive neural network (NN) control law for a missile system with unknown parameters and hysteresis input is proposed based on a backstepping technique. The controller is designed by introducing NN approximation, which can be adjusted by an adaptive law based on the backstepping approach. The developed NN controller does not require a priori knowledge of the unknown backlash hysteresis. In particular, unlike existing results on adaptive compensation for unknown backlash hysteresis, the sign of b is no longer needed. It is shown that the designed controller can ensure the stability and tracking performance of the closed-loop system.
topic Missle system
adaptive control
neural network
hysteresis
autopilot
aerodynamics
url https://ieeexplore.ieee.org/document/7982596/
work_keys_str_mv AT jianpingcai adaptiveneuralnetworkcontrolformissilesystemswithunknownhysteresisinput
AT lantaoxing adaptiveneuralnetworkcontrolformissilesystemswithunknownhysteresisinput
AT mengzhang adaptiveneuralnetworkcontrolformissilesystemswithunknownhysteresisinput
AT lujuanshen adaptiveneuralnetworkcontrolformissilesystemswithunknownhysteresisinput
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