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

• Published in: International journal of control, automation, and systems Vol. 19; no. 2; pp. 687 - 697
• Main Authors: Wang, Jianhui, Zhu, Peisen, He, Biaotao, Deng, Guiyang, Zhang, Chunliang, Huang, Xing
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.02.2021; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Adaptive control; Control; Control methods; Control stability; Disturbances; Engineering; Mechatronics; Neural networks; Nonlinear systems; Nonlinearity; Regular Papers; Robotics; Sliding mode control; 제어계측공학; sliding mode; backstepping; Adaptive control; extended state observer(ESO); neural network
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-019-0972-x

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.