Backstepping sliding mode control for uncertain strictfeedback nonlinear systems using neural-network-based adaptive gain scheduling

doi:10.21629/JSEE.2018.03.15

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (3): 580-586.doi: 10.21629/JSEE.2018.03.15

• Control Theory and Application • Previous Articles Next Articles

Backstepping sliding mode control for uncertain strictfeedback nonlinear systems using neural-network-based adaptive gain scheduling

Yueneng YANG*(), Ye YAN()

College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China

Received:2016-05-14 Online:2018-06-28 Published:2018-07-02
Contact: Yueneng YANG E-mail:yangyueneng@163.com;yeyan_kjs@163.com
About author:YANG Yueneng was born in 1984. He received his B.S. and M.S. degrees in civil engineering from National University of Defense Technology, Changsha, China, in 2006 and 2008, respectively, and his Ph.D. degree in aeronautical and astronautical science and technology from National University of Defense Technology, Changsha, in 2013. He is now a lecturer in College of Aerospace Science and Engineering, National University of Defense Technology. His research interests include nonlinear control, sliding mode control and intelligent control. E-mail: yangyueneng@163.com|YAN Ye was born in 1971. He received his B.S., M.S. and Ph.D. degrees in aeronautical and astronautical science and technology from National University of Defense Technology, Changsha, China, in 1994, 1997 and 2006, respectively. He is currently serving as a professor of College of Aerospace Science and Engineering, National University of Defense Technology. His research interests include aircraft design, guidance, navigation and control. E-mail: yeyan_kjs@163.com
Supported by:
the National Natural Science Foundation of China(11502288);the Natural Science Foundation of Hunan Province(2016JJ3019);the Aeronautical Science Foundation of China(2017ZA88001);the Scientific Research Project of National University of Defense Technology(ZK17-03-32);This work was supported by the National Natural Science Foundation of China (11502288), the Natural Science Foundation of Hunan Province (2016JJ3019), the Aeronautical Science Foundation of China (2017ZA88001) and the Scientific Research Project of National University of Defense Technology (ZK17-03-32)

Abstract

Abstract:

A neural-network-based adaptive gain scheduling backstepping sliding mode control (NNAGS-BSMC) approach for a class of uncertain strict-feedback nonlinear system is proposed. First, the control problem of uncertain strict-feedback nonlinear systems is formulated. Second, the detailed design of NNAGSBSMC is described. The sliding mode control (SMC) law is designed to track a referenced output via backstepping technique. To decrease chattering result from SMC, a radial basis function neural network (RBFNN) is employed to construct the NNAGSBSMC to facilitate adaptive gain scheduling, in which the gains are scheduled adaptively via neural network (NN), with sliding surface and its differential as NN inputs and the gains as NN outputs. Finally, the verification example is given to show the effectiveness and robustness of the proposed approach. Contrasting simulation results indicate that the NNAGS-BSMC decreases the chattering effectively and has better control performance against the BSMC.

Key words: backstepping control, sliding mode control (SMC), neural network (NN), strict-feedback system, chattering decrease

Yueneng YANG, Ye YAN. Backstepping sliding mode control for uncertain strictfeedback nonlinear systems using neural-network-based adaptive gain scheduling[J]. Journal of Systems Engineering and Electronics, 2018, 29(3): 580-586.

Figures/Tables 4

References 26

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Backstepping sliding mode control for uncertain strictfeedback nonlinear systems using neural-network-based adaptive gain scheduling

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