An Adaptive Neural Sliding Mode Control with ESO for Uncertain Nonlinear Systems

An adaptive neural sliding mode control with ESO for uncertain nonlinear systems is proposed to improve the stability of the control system. Any control system inevitably exists uncertain disturbances and nonlinearities which severely affect the control performance and stability. Neural network can...

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Bibliographic Details
Published in:International journal of control, automation, and systems 2021, Automation, and Systems, 19(2), , pp.687-697
Main Authors: Wang, Jianhui, Zhu, Peisen, He, Biaotao, Deng, Guiyang, Zhang, Chunliang, Huang, Xing
Format: Article
Language:English
Subjects:
Adaptive control
Control
Control methods
Control stability
Disturbances
Engineering
Mechatronics
Neural networks
Nonlinear systems
Nonlinearity
Regular Papers
Robotics
Sliding mode control
제어계측공학
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Summary:An adaptive neural sliding mode control with ESO for uncertain nonlinear systems is proposed to improve the stability of the control system. Any control system inevitably exists uncertain disturbances and nonlinearities which severely affect the control performance and stability. Neural network can be utilized to approximate the uncertain nonlinearities. Nevertheless, it produces approximate errors, which will become more difficult to deal with as the order of the system increases. Moreover, these errors and uncertain disturbances will result in a consequence that the control system can be unable to converge quickly, and has to deal with a lot of calculations. Therefore, in order to perfect the performance and stability of the control system, this paper combines sliding mode control and ESO, and designs an adaptive neural control method. The simulation results illustrate that the improved system has superior tracking performance and anti-interference ability.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-019-0972-x