Regular Papers

International Journal of Control, Automation and Systems 2021; 19(2): 687-697

Published online September 15, 2020

https://doi.org/10.1007/s12555-019-0972-x

© The International Journal of Control, Automation, and Systems

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

Jianhui Wang*, Peisen Zhu, Biaotao He, Guiyang Deng, Chunliang Zhang*, and Xing Huang

Guangzhou University

Abstract

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.

Keywords Adaptive control, backstepping, extended state observer(ESO), neural network, sliding mode.

Article

Regular Papers

International Journal of Control, Automation and Systems 2021; 19(2): 687-697

Published online February 1, 2021 https://doi.org/10.1007/s12555-019-0972-x

Copyright © The International Journal of Control, Automation, and Systems.

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

Jianhui Wang*, Peisen Zhu, Biaotao He, Guiyang Deng, Chunliang Zhang*, and Xing Huang

Guangzhou University

Abstract

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.

Keywords: Adaptive control, backstepping, extended state observer(ESO), neural network, sliding mode.

IJCAS
April 2024

Vol. 22, No. 4, pp. 1105~1460

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